problems
dict |
|---|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Water-borne diseases such as diarrhea, cholera, typhoid, and hepatitis A are prevalent in many rural areas and tribal belts of the Northeastern Region (NER), especially during the monsoon season. These outbreaks are often linked to contaminated water sources, poor sanitation infrastructure, and delayed medical response. The terrain and remoteness of many villages make it difficult for health workers to monitor and respond to emerging health threats in time.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "A digital health platform that includes:\n\n• A mobile app for data collection and community health reporting.\n• AI-based outbreak prediction engine using health and environmental data.\n• Integration with low-cost water quality sensors or manual test kits.\n• Alert system for health authorities and local leaders.\n• Educational modules for hygiene awareness and disease prevention.\n• Offline functionality and support for tribal languages.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "This problem statement proposes the development of a Smart Health Surveillance and Early Warning System that can detect, monitor, and help prevent outbreaks of water-borne diseases in vulnerable communities. The system can be:\n\n• Collect health data from local clinics, ASHA workers, and community volunteers via mobile apps or SMS.\n• Use AI/ML models to detect patterns and predict potential outbreaks based on symptoms, water quality reports, and seasonal trends.\n• Integrate with water testing kits or IoT sensors to monitor water source contamination (e.g., turbidity, pH, bacterial presence).\n• Provide real-time alerts to district health officials and local governance bodies.\n• Include a multilingual mobile interface for community reporting and awareness campaigns.\n• Offer dashboards for health departments to visualize hotspots, track interventions, and allocate resources.",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Development of North Eastern Region",
"problem_description": "This problem statement proposes the development of a Smart Health Surveillance and Early Warning System that can detect, monitor, and help prevent outbreaks of water-borne diseases in vulnerable communities. The system can be:\n\n• Collect health data from local clinics, ASHA workers, and community volunteers via mobile apps or SMS.\n• Use AI/ML models to detect patterns and predict potential outbreaks based on symptoms, water quality reports, and seasonal trends.\n• Integrate with water testing kits or IoT sensors to monitor water source contamination (e.g., turbidity, pH, bacterial presence).\n• Provide real-time alerts to district health officials and local governance bodies.\n• Include a multilingual mobile interface for community reporting and awareness campaigns.\n• Offer dashboards for health departments to visualize hotspots, track interventions, and allocate resources.\n\nBackground\n\nWater-borne diseases such as diarrhea, cholera, typhoid, and hepatitis A are prevalent in many rural areas and tribal belts of the Northeastern Region (NER), especially during the monsoon season. These outbreaks are often linked to contaminated water sources, poor sanitation infrastructure, and delayed medical response. The terrain and remoteness of many villages make it difficult for health workers to monitor and respond to emerging health threats in time.\n\nExpected Solution\n\nA digital health platform that includes:\n\n• A mobile app for data collection and community health reporting.\n• AI-based outbreak prediction engine using health and environmental data.\n• Integration with low-cost water quality sensors or manual test kits.\n• Alert system for health authorities and local leaders.\n• Educational modules for hygiene awareness and disease prevention.\n• Offline functionality and support for tribal languages.",
"ps_number": "SIH25001",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "MedTech / BioTech / HealthTech",
"title": "Smart Community Health Monitoring and Early Warning System for Water-Borne Diseases in Rural Northeast India"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": "• Detect sudden location drop-offs, prolonged inactivity, or deviation from planned routes.\n• Flag missing, silent, or distress behaviour for investigations.",
"background": "In regions like the Northeast, where tourism is a key economic driver, ensuring the safety of visitors is paramount. Traditional policing and manual tracking methods are insufficient in remote and high-risk areas. There is a pressing need for a smart, technology-driven solution that ensures real-time monitoring, rapid response, and secure identity verification for tourists, while maintaining privacy and ease of travel.",
"conclusion": null,
"data_privacy_&_security": "• End-to-end encryption and compliance with data protection laws.\n• Blockchain ensures tamper-proof identity and travel records.",
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": "• A secure blockchain-based system that issues digital IDs to tourists at entry points (airports, hotels, check-posts).\n• These IDs should include basic KYC (Aadhaar/passport), trip itinerary, and emergency contacts, and be valid only for the duration of the visit.",
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "A robust digital ecosystem comprising:\n\n• Web portal and mobile app for tourists and authorities.\n• AI/ML models for behaviour tracking and predictive alerts.\n• Blockchain-based ID generation and verification.\n• Real-time dashboards for police/tourism departments.\n• Optional IoT wearable integration for enhanced safety.\n• Automated alert dispatch and evidence logging systems.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "This problem statement proposes the development of a Smart Tourist Safety Monitoring & Incident Response System that leverages AI, Blockchain, and Geo-Fencing technologies. The system should include:",
"iot_integration_optional": "• Smart bands or tags for tourists in high-risk areas (e.g., caves, forests).\n• Continuous health/location signals and manual SOS feature.",
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": "• Auto-assign a Tourist Safety Score based on travel patterns and area sensitivity.\n• Geo-fencing alerts when tourists enter high-risk or restricted zones.\n• Panic Button with live location sharing to nearest police unit and emergency contacts.\n• Optional real-time tracking feature (opt-in) for families and law enforcement.",
"multilingual_support": "• App and platform available in 10+ Indian languages and English.\n• Voice/text emergency access for elderly or disabled travellers.",
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": "• Real-time visualizations of tourist clusters and heat maps of high-risk zones.\n• Access to digital ID records, alert history, and last known locations.\n• Automated E-FIR generation for missing person cases."
},
"organization": "Ministry of Development of North Eastern Region",
"problem_description": "This problem statement proposes the development of a Smart Tourist Safety Monitoring & Incident Response System that leverages AI, Blockchain, and Geo-Fencing technologies. The system should include:\n\nDigital Tourist ID Generation Platform\n• A secure blockchain-based system that issues digital IDs to tourists at entry points (airports, hotels, check-posts).\n• These IDs should include basic KYC (Aadhaar/passport), trip itinerary, and emergency contacts, and be valid only for the duration of the visit.\n\nMobile Application for Tourists\n• Auto-assign a Tourist Safety Score based on travel patterns and area sensitivity.\n• Geo-fencing alerts when tourists enter high-risk or restricted zones.\n• Panic Button with live location sharing to nearest police unit and emergency contacts.\n• Optional real-time tracking feature (opt-in) for families and law enforcement.\n\nAI-Based Anomaly Detection\n• Detect sudden location drop-offs, prolonged inactivity, or deviation from planned routes.\n• Flag missing, silent, or distress behaviour for investigations.\n\nTourism Department & Police Dashboard\n• Real-time visualizations of tourist clusters and heat maps of high-risk zones.\n• Access to digital ID records, alert history, and last known locations.\n• Automated E-FIR generation for missing person cases.\n\nIoT Integration (Optional)\n• Smart bands or tags for tourists in high-risk areas (e.g., caves, forests).\n• Continuous health/location signals and manual SOS feature.\n\nMultilingual Support\n• App and platform available in 10+ Indian languages and English.\n• Voice/text emergency access for elderly or disabled travellers.\n\nData Privacy & Security\n• End-to-end encryption and compliance with data protection laws.\n• Blockchain ensures tamper-proof identity and travel records.\n\nBackground\n\nIn regions like the Northeast, where tourism is a key economic driver, ensuring the safety of visitors is paramount. Traditional policing and manual tracking methods are insufficient in remote and high-risk areas. There is a pressing need for a smart, technology-driven solution that ensures real-time monitoring, rapid response, and secure identity verification for tourists, while maintaining privacy and ease of travel.\n\nExpected Solution\n\nA robust digital ecosystem comprising:\n\n• Web portal and mobile app for tourists and authorities.\n• AI/ML models for behaviour tracking and predictive alerts.\n• Blockchain-based ID generation and verification.\n• Real-time dashboards for police/tourism departments.\n• Optional IoT wearable integration for enhanced safety.\n• Automated alert dispatch and evidence logging systems.",
"ps_number": "SIH25002",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Travel & Tourism",
"title": "Smart Tourist Safety Monitoring & Incident Response System using AI Geo-Fencing and Blockchain-based Digital ID"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "The Northeastern Region faces persistent challenges in building and maintaining rural roads due to difficult terrain, high rainfall, and limited access to conventional construction materials. Simultaneously, plastic waste management remains a growing concern in both urban and semi-urban pockets of the region. Bamboo, abundantly available in NER, offers high tensile strength and flexibility, while recycled plastic enhances water resistance and durability when used in road surfacing. Combining these two materials presents a unique opportunity to create sustainable, modular road infrastructure tailored for NER's needs.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "• A prototype modular panel system combining bamboo and recycled plastic.\n• A deployment guide for constructing 100-200 meter stretches in rural terrain.\n• Cost-benefit analysis compared to conventional road-building methods.\n• Environmental impact assessment and scalability roadmap.\n• Optional integration with IoT-based monitoring for wear and tear tracking.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "This problem statement proposes the design and development of prefabricated modular road panels that use bamboo reinforcement and plastic waste-infused composites to build durable, erosion-resistant roads in remote and hilly areas. The solution may be:\n\n• Utilize bamboo mesh or strips as structural reinforcement within concrete or stabilized soil panels.\n• Incorporate processed plastic waste (Low-Density Polyethylene - LDPE, High-Density Polyethylene - HDPE) into the mix to improve water resistance and flexibility.\n• Include drainage features, slope adaptation mechanisms, and anti-slip surfacing.",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Development of North Eastern Region",
"problem_description": "This problem statement proposes the design and development of prefabricated modular road panels that use bamboo reinforcement and plastic waste-infused composites to build durable, erosion-resistant roads in remote and hilly areas. The solution may be:\n\n• Utilize bamboo mesh or strips as structural reinforcement within concrete or stabilized soil panels.\n• Incorporate processed plastic waste (Low-Density Polyethylene - LDPE, High-Density Polyethylene - HDPE) into the mix to improve water resistance and flexibility.\n• Include drainage features, slope adaptation mechanisms, and anti-slip surfacing.\n\nBackground\n\nThe Northeastern Region faces persistent challenges in building and maintaining rural roads due to difficult terrain, high rainfall, and limited access to conventional construction materials. Simultaneously, plastic waste management remains a growing concern in both urban and semi-urban pockets of the region. Bamboo, abundantly available in NER, offers high tensile strength and flexibility, while recycled plastic enhances water resistance and durability when used in road surfacing. Combining these two materials presents a unique opportunity to create sustainable, modular road infrastructure tailored for NER's needs.\n\nExpected Solution\n\n• A prototype modular panel system combining bamboo and recycled plastic.\n• A deployment guide for constructing 100-200 meter stretches in rural terrain.\n• Cost-benefit analysis compared to conventional road-building methods.\n• Environmental impact assessment and scalability roadmap.\n• Optional integration with IoT-based monitoring for wear and tear tracking.",
"ps_number": "SIH25003",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Transportation & Logistics",
"title": "Low-Cost smart transportation solution for Agri produce from remote farms to nearest motorable road in NER Region"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "The Government of India is implementing the Bharat Pashudhan App (BPA) for systematic data recording of breeding, health, and nutrition of dairy animals. Field Level Workers (FLWs) are responsible for capturing animal data on the ground. However, despite multiple training programs, a recurring issue is the incorrect identification and registration of animal breeds of cattle and buffaloes. This misclassification significantly affects the integrity and usability of the data for research, policy planning, and targeted interventions.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Breed identification errors in BPA often arise due to manual judgment and lack of breed-specific awareness among FLWs. India, being home to a diverse array of indigenous and crossbred cattle and buffalo breeds, presents a complex challenge for accurate breed identification. Incorrect entries compromise the value of collected data and, in turn, impact the effectiveness of genetic improvement, nutrition planning, disease control, and overall program outcomes.\n\nTo address this, an AI-driven solution that can identify the breed of an animal using its image can prove extremely valuable. By using image recognition and machine learning techniques, the software can standardize breed identification and minimize manual errors. If successfully developed and validated, such a system can be integrated with the BPA to act as a decision-support tool for FLWs during registration.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "• Uses Artificial Intelligence (AI) and image analysis to recognize and classify the breed of cattle and buffaloes based on images.\n• Can handle diverse environmental backgrounds, lighting conditions, and varying animal poses.\n• Maintains a breed database (for the most common Indian cattle and buffalo breeds and their crosses).\n• Provides breed suggestions or confirmations at the time of registration in BPA.\n• Can be seamlessly integrated with the BPA platform to support real-time validation or correction of breed entries.\n• Includes a user-friendly interface for FLWs with minimal technical training requirements.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Fisheries Animal Husbandry & Dairying",
"problem_description": "Background\n\nThe Government of India is implementing the Bharat Pashudhan App (BPA) for systematic data recording of breeding, health, and nutrition of dairy animals. Field Level Workers (FLWs) are responsible for capturing animal data on the ground. However, despite multiple training programs, a recurring issue is the incorrect identification and registration of animal breeds of cattle and buffaloes. This misclassification significantly affects the integrity and usability of the data for research, policy planning, and targeted interventions.\n\nDescription\n\nBreed identification errors in BPA often arise due to manual judgment and lack of breed-specific awareness among FLWs. India, being home to a diverse array of indigenous and crossbred cattle and buffalo breeds, presents a complex challenge for accurate breed identification. Incorrect entries compromise the value of collected data and, in turn, impact the effectiveness of genetic improvement, nutrition planning, disease control, and overall program outcomes.\n\nTo address this, an AI-driven solution that can identify the breed of an animal using its image can prove extremely valuable. By using image recognition and machine learning techniques, the software can standardize breed identification and minimize manual errors. If successfully developed and validated, such a system can be integrated with the BPA to act as a decision-support tool for FLWs during registration.\n\nExpected Solution\n\n• Uses Artificial Intelligence (AI) and image analysis to recognize and classify the breed of cattle and buffaloes based on images.\n• Can handle diverse environmental backgrounds, lighting conditions, and varying animal poses.\n• Maintains a breed database (for the most common Indian cattle and buffalo breeds and their crosses).\n• Provides breed suggestions or confirmations at the time of registration in BPA.\n• Can be seamlessly integrated with the BPA platform to support real-time validation or correction of breed entries.\n• Includes a user-friendly interface for FLWs with minimal technical training requirements.",
"ps_number": "SIH25004",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "Image based breed recognition for cattle and buffaloes of India"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "Background: In dairy farming, evaluating the body structure of animals is vital to predict their longevity, productivity, and reproductive efficiency. Traditionally, Animal Type Classification (ATC)—which involves scoring animals for physical traits—is conducted by trained personnel through visual inspection and manual measurement of specific body parts. However, this manual method is prone to human error and subjectivity, even with trained professionals, resulting in inconsistent and potentially unreliable data.\n\nWith advances in Artificial Intelligence (AI) and image processing technologies, there is an opportunity to automate this process. Automated scoring based on images can ensure standardization, minimize observer bias, and improve the reliability of data captured for scientific and breeding purposes.\n\nDescription: The Government of India is implementing the Rashtriya Gokul Mission (RGM) since December 2014, aiming to conserve and develop indigenous bovine breeds, genetically upgrade the bovine population, and enhance milk productivity. Under this mission, Progeny Testing (PT) and Pedigree Selection (PS) programs are being carried out in key dairy breeds across the country to produce high genetic merit bulls for breeding purposes.\n\nAnimal Type Classification (ATC) is a crucial step in identifying top-performing elite dams, which are potential mothers of future breeding bulls. Currently, ATC is performed manually by a trained Animal Typer who visually examines and measures physical traits, and then records the scores in the Bharat Pashudhan App (BPA). Despite training, errors due to fatigue, bias, or measurement inaccuracies can adversely affect data quality and scientific analysis.\n\nThere is a need for an AI-driven solution that can automate this classification process by analyzing animal images, extracting body structure parameters, and assigning standardized scores with minimal human intervention. If integrated with BPA, such a solution would enhance the accuracy, efficiency, and scientific validity of animal evaluation under PT and PS programs.\n\nExpected Solution: Students are expected to develop an AI-based Auto Recording of Animal Type Classification System that can:\n\nUse images of cattle and buffaloes to evaluate physical traits relevant to Animal Type Classification.\nExtract and quantify specific body structure parameters (e.g., body length, height at withers, chest width, rump angle, etc.) using AI and image processing techniques.\nGenerate objective and consistent classification scores.\nAuto-record and store the classification data in a structured format.\nProvide seamless integration with BPA to auto-save the classification records at the time of evaluation.\nBe user-friendly and operable by field personnel with minimal technical skills.",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Fisheries Animal Husbandry & Dairying",
"problem_description": "Background: In dairy farming, evaluating the body structure of animals is vital to predict their longevity, productivity, and reproductive efficiency. Traditionally, Animal Type Classification (ATC)—which involves scoring animals for physical traits—is conducted by trained personnel through visual inspection and manual measurement of specific body parts. However, this manual method is prone to human error and subjectivity, even with trained professionals, resulting in inconsistent and potentially unreliable data.\n\nWith advances in Artificial Intelligence (AI) and image processing technologies, there is an opportunity to automate this process. Automated scoring based on images can ensure standardization, minimize observer bias, and improve the reliability of data captured for scientific and breeding purposes.\n\nDescription: The Government of India is implementing the Rashtriya Gokul Mission (RGM) since December 2014, aiming to conserve and develop indigenous bovine breeds, genetically upgrade the bovine population, and enhance milk productivity. Under this mission, Progeny Testing (PT) and Pedigree Selection (PS) programs are being carried out in key dairy breeds across the country to produce high genetic merit bulls for breeding purposes.\n\nAnimal Type Classification (ATC) is a crucial step in identifying top-performing elite dams, which are potential mothers of future breeding bulls. Currently, ATC is performed manually by a trained Animal Typer who visually examines and measures physical traits, and then records the scores in the Bharat Pashudhan App (BPA). Despite training, errors due to fatigue, bias, or measurement inaccuracies can adversely affect data quality and scientific analysis.\n\nThere is a need for an AI-driven solution that can automate this classification process by analyzing animal images, extracting body structure parameters, and assigning standardized scores with minimal human intervention. If integrated with BPA, such a solution would enhance the accuracy, efficiency, and scientific validity of animal evaluation under PT and PS programs.\n\nExpected Solution: Students are expected to develop an AI-based Auto Recording of Animal Type Classification System that can:\n\nUse images of cattle and buffaloes to evaluate physical traits relevant to Animal Type Classification.\nExtract and quantify specific body structure parameters (e.g., body length, height at withers, chest width, rump angle, etc.) using AI and image processing techniques.\nGenerate objective and consistent classification scores.\nAuto-record and store the classification data in a structured format.\nProvide seamless integration with BPA to auto-save the classification records at the time of evaluation.\nBe user-friendly and operable by field personnel with minimal technical skills.",
"ps_number": "SIH25005",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "Image based Animal Type Classification for cattle and buffaloes"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Biosecurity is a cornerstone of animal health management, particularly in the pig and poultry sectors, where disease outbreaks such as Avian Influenza and African Swine Fever can cause significant economic losses, threaten food security, and disrupt rural livelihoods. Despite its importance, many farmers—especially smallholders in resource-limited areas—struggle to access practical, actionable information on biosecurity protocols, risk assessment tools, and regulatory compliance requirements.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• Enhanced farmer awareness and education on biosecurity.\n• Improved risk management at the farm level as well as self-assessment.\n• Easy access to customized biosecurity protocols and guidelines.\n• Digital record-keeping and compliance tracking.\n• Timely alerts and disease notifications to farmers.\n• Healthier livestock and increased farm productivity.\n• Empowerment of small and marginal farmers with limited resources.\n• Support to authorities in data-driven surveillance and policy making.\n• Stronger collaboration across the livestock ecosystem.\n• Improved national preparedness for zoonotic and transboundary diseases.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "There is an urgent need for a user-friendly, digital platform that empowers farmers to implement, monitor, and sustain robust biosecurity practices on their farms. This portal should offer end-to-end solutions for farm-level biosecurity management by integrating:\n\n• Customizable risk assessment tools based on local epidemiological conditions.\n• Interactive training modules and best practice guidelines tailored for pig and poultry production systems.\n• Compliance tracking features aligned with regulatory frameworks to help farmers work toward disease-free compartment recognition.\n• Real-time alerts and monitoring dashboards for disease outbreaks and biosecurity breaches.\n• Multilingual and mobile-first design to ensure accessibility in remote and rural areas.\n\nThe platform should also enable data collection and analysis for policy support, foster collaborative networking among stakeholders (farmers, veterinarians, extension workers, etc.), and promote long-term resilience and sustainability in the livestock sector.",
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Fisheries Animal Husbandry & Dairying",
"problem_description": "Background\n\nBiosecurity is a cornerstone of animal health management, particularly in the pig and poultry sectors, where disease outbreaks such as Avian Influenza and African Swine Fever can cause significant economic losses, threaten food security, and disrupt rural livelihoods. Despite its importance, many farmers—especially smallholders in resource-limited areas—struggle to access practical, actionable information on biosecurity protocols, risk assessment tools, and regulatory compliance requirements.\n\nProblem Description\n\nThere is an urgent need for a user-friendly, digital platform that empowers farmers to implement, monitor, and sustain robust biosecurity practices on their farms. This portal should offer end-to-end solutions for farm-level biosecurity management by integrating:\n\n• Customizable risk assessment tools based on local epidemiological conditions.\n• Interactive training modules and best practice guidelines tailored for pig and poultry production systems.\n• Compliance tracking features aligned with regulatory frameworks to help farmers work toward disease-free compartment recognition.\n• Real-time alerts and monitoring dashboards for disease outbreaks and biosecurity breaches.\n• Multilingual and mobile-first design to ensure accessibility in remote and rural areas.\n\nThe platform should also enable data collection and analysis for policy support, foster collaborative networking among stakeholders (farmers, veterinarians, extension workers, etc.), and promote long-term resilience and sustainability in the livestock sector.\n\nExpected Outcomes\n\n• Enhanced farmer awareness and education on biosecurity.\n• Improved risk management at the farm level as well as self-assessment.\n• Easy access to customized biosecurity protocols and guidelines.\n• Digital record-keeping and compliance tracking.\n• Timely alerts and disease notifications to farmers.\n• Healthier livestock and increased farm productivity.\n• Empowerment of small and marginal farmers with limited resources.\n• Support to authorities in data-driven surveillance and policy making.\n• Stronger collaboration across the livestock ecosystem.\n• Improved national preparedness for zoonotic and transboundary diseases.",
"ps_number": "SIH25006",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "Development of a Digital Farm Management Portal for Implementing Biosecurity Measures in Pig and Poultry Farms"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Antimicrobials are vital in ensuring animal health and productivity in modern livestock systems. However, inappropriate and excessive use of these drugs can result in antimicrobial residues in animal-derived food products and contribute to the rise of antimicrobial resistance (AMR)—a major global threat to both animal and public health. Monitoring antimicrobial usage (AMU) and ensuring compliance with Maximum Residue Limits (MRL) is crucial to promoting responsible drug use and safeguarding food safety.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• Improved antimicrobial stewardship practices at the farm level.\n• Enhanced compliance with MRL norms and regulatory frameworks.\n• Real-time availability of data on AMU for authorities and stakeholders.\n• Data-driven decision-making to inform policy and farm-level action.\n• Capability for trend analysis and automated reporting.\n• Healthier livestock and reduction in antimicrobial residues in food.\n• Improved public health protection and consumer confidence.\n• Contribution to global AMR reduction efforts.\n• Better engagement of farmers and veterinarians through digital tools.\n• Promotion of sustainable and responsible livestock farming practices.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "There is a growing need for a digital solution that can help track, monitor, and manage antimicrobial usage across livestock farms while ensuring adherence to MRL standards. The proposed system should serve as a centralized digital portal that facilitates:\n\n• Recording and tracking the types, dosages, frequency, and reasons for antimicrobial use in animals both in treatment and through feed.\n• Integration with veterinary prescriptions and treatment logs to monitor compliance.\n• Alert systems for withdrawal periods and MRL compliance prior to the sale or processing of animal products.\n• Real-time dashboards and data visualization tools for analysis of AMU trends across species, regions, and time periods.\n• Use of blockchain or other secure technologies to ensure data integrity and traceability.\n• Mobile app interfaces for easy data entry by farmers and veterinarians in field conditions.\n\nThis digital platform would contribute to improved antimicrobial stewardship, better regulatory enforcement, and help India align with global AMR mitigation strategies, while also strengthening public trust in livestock products.",
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Fisheries Animal Husbandry & Dairying",
"problem_description": "Background\n\nAntimicrobials are vital in ensuring animal health and productivity in modern livestock systems. However, inappropriate and excessive use of these drugs can result in antimicrobial residues in animal-derived food products and contribute to the rise of antimicrobial resistance (AMR)—a major global threat to both animal and public health. Monitoring antimicrobial usage (AMU) and ensuring compliance with Maximum Residue Limits (MRL) is crucial to promoting responsible drug use and safeguarding food safety.\n\nProblem Description\n\nThere is a growing need for a digital solution that can help track, monitor, and manage antimicrobial usage across livestock farms while ensuring adherence to MRL standards. The proposed system should serve as a centralized digital portal that facilitates:\n\n• Recording and tracking the types, dosages, frequency, and reasons for antimicrobial use in animals both in treatment and through feed.\n• Integration with veterinary prescriptions and treatment logs to monitor compliance.\n• Alert systems for withdrawal periods and MRL compliance prior to the sale or processing of animal products.\n• Real-time dashboards and data visualization tools for analysis of AMU trends across species, regions, and time periods.\n• Use of blockchain or other secure technologies to ensure data integrity and traceability.\n• Mobile app interfaces for easy data entry by farmers and veterinarians in field conditions.\n\nThis digital platform would contribute to improved antimicrobial stewardship, better regulatory enforcement, and help India align with global AMR mitigation strategies, while also strengthening public trust in livestock products.\n\nExpected Outcomes\n\n• Improved antimicrobial stewardship practices at the farm level.\n• Enhanced compliance with MRL norms and regulatory frameworks.\n• Real-time availability of data on AMU for authorities and stakeholders.\n• Data-driven decision-making to inform policy and farm-level action.\n• Capability for trend analysis and automated reporting.\n• Healthier livestock and reduction in antimicrobial residues in food.\n• Improved public health protection and consumer confidence.\n• Contribution to global AMR reduction efforts.\n• Better engagement of farmers and veterinarians through digital tools.\n• Promotion of sustainable and responsible livestock farming practices.",
"ps_number": "SIH25007",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "Development of a Digital Farm Management Portal for Monitoring Maximum Residue Limits (MRL) and Antimicrobial Usage (AMU) in Livestock"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• A digital platform or app that offers interactive disaster education modules, region-specific alerts, and virtual drills.\n• Gamified learning experiences to improve engagement.\n• Emergency contact directories and real-time communication tools during disasters.\n• Dashboards for school administrators to track preparedness scores and drill participation.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": "Lack of awareness and preparedness leads to panic, chaos, and potentially fatal outcomes during emergencies. By integrating disaster education into regular learning, institutions can equip students and staff with life-saving knowledge and skills. This is especially critical in areas prone to natural calamities.\n\nEmpowering young people with this knowledge not only makes campuses safer but also contributes to a more disaster-resilient society.",
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "In India, schools and colleges are often unprepared for natural disasters such as earthquakes, floods, and fires. While emergency guidelines exist on paper, there is a lack of structured disaster management education integrated into the curriculum. Institutions lack digital tools to simulate disaster scenarios or conduct virtual drills to train students and staff on safety protocols.\n\nFurthermore, there’s a gap in localized awareness—many students are unaware of how to react during disasters specific to their region. Manual drills, where they occur, are infrequent and often poorly coordinated, failing to instill practical preparedness.",
"relevant_stakeholders___beneficiaries": "• Students (K-12 and higher education)\n• Teachers and administrative staff\n• Educational institutions and local disaster response teams\n• Parents and guardians\n• Government departments (NDMA, Education Ministry)",
"supporting_data": "• NDMA reports show low awareness levels in schools despite India’s high disaster vulnerability index.\n• UNDRR has recommended integrating disaster risk reduction in education policies (Ref: National Disaster Management Authority).",
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nIn India, schools and colleges are often unprepared for natural disasters such as earthquakes, floods, and fires. While emergency guidelines exist on paper, there is a lack of structured disaster management education integrated into the curriculum. Institutions lack digital tools to simulate disaster scenarios or conduct virtual drills to train students and staff on safety protocols.\n\nFurthermore, there’s a gap in localized awareness—many students are unaware of how to react during disasters specific to their region. Manual drills, where they occur, are infrequent and often poorly coordinated, failing to instill practical preparedness.\n\nImpact / Why this problem needs to be solved\n\nLack of awareness and preparedness leads to panic, chaos, and potentially fatal outcomes during emergencies. By integrating disaster education into regular learning, institutions can equip students and staff with life-saving knowledge and skills. This is especially critical in areas prone to natural calamities.\n\nEmpowering young people with this knowledge not only makes campuses safer but also contributes to a more disaster-resilient society.\n\nExpected Outcomes\n\n• A digital platform or app that offers interactive disaster education modules, region-specific alerts, and virtual drills.\n• Gamified learning experiences to improve engagement.\n• Emergency contact directories and real-time communication tools during disasters.\n• Dashboards for school administrators to track preparedness scores and drill participation.\n\nRelevant Stakeholders / Beneficiaries\n\n• Students (K-12 and higher education)\n• Teachers and administrative staff\n• Educational institutions and local disaster response teams\n• Parents and guardians\n• Government departments (NDMA, Education Ministry)\n\nSupporting Data\n\n• NDMA reports show low awareness levels in schools despite India’s high disaster vulnerability index.\n• UNDRR has recommended integrating disaster risk reduction in education policies (Ref: National Disaster Management Authority).",
"ps_number": "SIH25008",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Disaster Management",
"title": "Disaster Preparedness and Response Education System for Schools and Colleges"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• A gamified mobile/web platform or app that teaches students about environmental issues through interactive lessons, challenges, quizzes, and real-world tasks (e.g., tree-planting, waste segregation).\n• Tracking of eco-points, enabling school-level competitions.\n• Rewards for sustainable practices through digital badges and recognition.",
"expected_solution": null,
"impact": "As future decision-makers, students must be environmentally literate and empowered to take meaningful actions. Without innovative education methods, we risk raising a generation unaware of sustainability challenges.\n\nAn interactive, practical approach to environmental learning will foster long-term behavioral change, local involvement, and a ripple effect across families and communities. This aligns with India's SDG goals and NEP 2020's emphasis on experiential learning.",
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "Despite the rising urgency of climate change and environmental degradation, environmental education remains largely theoretical in many Indian schools and colleges. Students are often taught textbook-based content with little emphasis on real-world application, local ecological issues, or personal responsibility.\n\nThere is a lack of engaging tools that motivate students to adopt eco-friendly practices or understand the direct consequences of their lifestyle choices. Traditional methods fail to instill sustainable habits or inspire youth participation in local environmental efforts.",
"relevant_stakeholders___beneficiaries": "• School and college students\n• Teachers and eco-club coordinators\n• Environmental NGOs and government departments",
"supporting_data": "• UNESCO reports that experiential, gamified learning increases student retention and engagement by over 70%.\n• NEP 2020 encourages integration of environmental awareness into the curriculum.",
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nDespite the rising urgency of climate change and environmental degradation, environmental education remains largely theoretical in many Indian schools and colleges. Students are often taught textbook-based content with little emphasis on real-world application, local ecological issues, or personal responsibility.\n\nThere is a lack of engaging tools that motivate students to adopt eco-friendly practices or understand the direct consequences of their lifestyle choices. Traditional methods fail to instill sustainable habits or inspire youth participation in local environmental efforts.\n\nImpact\n\nAs future decision-makers, students must be environmentally literate and empowered to take meaningful actions. Without innovative education methods, we risk raising a generation unaware of sustainability challenges.\n\nAn interactive, practical approach to environmental learning will foster long-term behavioral change, local involvement, and a ripple effect across families and communities. This aligns with India's SDG goals and NEP 2020's emphasis on experiential learning.\n\nExpected Outcomes\n\n• A gamified mobile/web platform or app that teaches students about environmental issues through interactive lessons, challenges, quizzes, and real-world tasks (e.g., tree-planting, waste segregation).\n• Tracking of eco-points, enabling school-level competitions.\n• Rewards for sustainable practices through digital badges and recognition.\n\nRelevant Stakeholders / Beneficiaries\n\n• School and college students\n• Teachers and eco-club coordinators\n• Environmental NGOs and government departments\n\nSupporting Data\n\n• UNESCO reports that experiential, gamified learning increases student retention and engagement by over 70%.\n• NEP 2020 encourages integration of environmental awareness into the curriculum.",
"ps_number": "SIH25009",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Gamified Environmental Education Platform for Schools and Colleges"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• A multilingual, AI-based mobile app or chatbot that provides real-time, location-specific crop advisory.\n• Soil health recommendations and fertilizer guidance.\n• Weather-based alerts and predictive insights.\n• Pest/disease detection via image uploads.\n• Market price tracking.\n• Voice support for low-literate users.\n• Feedback and usage data collection for continuous improvement.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": "Helping small farmers make informed decisions can significantly increase productivity, reduce costs, and improve livelihoods. It also contributes to sustainable farming practices, food security, and environmental conservation. A smart advisory solution can empower farmers with scientific insights in their native language and reduce dependency on unreliable third-party advice.",
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "A majority of small and marginal farmers in India rely on traditional knowledge, local shopkeepers, or guesswork for crop selection, pest control, and fertilizer use. They lack access to personalized, real-time advisory services that account for soil type, weather conditions, and crop history.\n\nThis often leads to poor yield, excessive input costs, and environmental degradation due to overuse of chemicals. Language barriers, low digital literacy, and absence of localized tools further limit their access to modern agri-tech resources.",
"relevant_stakeholders___beneficiaries": "• Small and marginal farmers\n• Agricultural extension officers\n• Government agriculture departments\n• NGOs and cooperatives\n• Agri-tech startups",
"supporting_data": "• 86% of Indian farmers are small or marginal (NABARD Report, 2022).\n• Studies show ICT-based advisories can increase crop yield by 20–30%.",
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nA majority of small and marginal farmers in India rely on traditional knowledge, local shopkeepers, or guesswork for crop selection, pest control, and fertilizer use. They lack access to personalized, real-time advisory services that account for soil type, weather conditions, and crop history.\n\nThis often leads to poor yield, excessive input costs, and environmental degradation due to overuse of chemicals. Language barriers, low digital literacy, and absence of localized tools further limit their access to modern agri-tech resources.\n\nImpact / Why this problem needs to be solved\n\nHelping small farmers make informed decisions can significantly increase productivity, reduce costs, and improve livelihoods. It also contributes to sustainable farming practices, food security, and environmental conservation. A smart advisory solution can empower farmers with scientific insights in their native language and reduce dependency on unreliable third-party advice.\n\nExpected Outcomes\n\n• A multilingual, AI-based mobile app or chatbot that provides real-time, location-specific crop advisory.\n• Soil health recommendations and fertilizer guidance.\n• Weather-based alerts and predictive insights.\n• Pest/disease detection via image uploads.\n• Market price tracking.\n• Voice support for low-literate users.\n• Feedback and usage data collection for continuous improvement.\n\nRelevant Stakeholders / Beneficiaries\n\n• Small and marginal farmers\n• Agricultural extension officers\n• Government agriculture departments\n• NGOs and cooperatives\n• Agri-tech startups\n\nSupporting Data\n\n• 86% of Indian farmers are small or marginal (NABARD Report, 2022).\n• Studies show ICT-based advisories can increase crop yield by 20–30%.",
"ps_number": "SIH25010",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "Smart Crop Advisory System for Small and Marginal Farmers"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• Automatically marks student attendance based on the daily timetable using QR code, Bluetooth/Wi-Fi proximity, or face recognition.\n• Displays real-time attendance on a classroom screen.\n• Suggests personalized academic tasks during free periods based on the student's interests, strengths, and career goals.\n• Generates a daily routine combining class schedule, free time, and long-term personal goals.\n\nThe app will require minimal infrastructure and be usable by both students and staff with basic training.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": "This issue impacts both administrative efficiency and student development. Automating attendance saves teachers' time and ensures more accurate records. Additionally, providing students with structured personal development activities during free time helps improve productivity, goal clarity, and learning outcomes. Institutions can also gain better insight into student behavior and engagement, allowing for more targeted support.",
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "Many educational institutions still depend on manual attendance systems, which are time-consuming and error-prone. Teachers spend a significant portion of class time marking attendance, reducing valuable instructional hours. Additionally, students often waste free periods with unproductive activities due to a lack of structured guidance. This leads to poor time management and reduced alignment with long-term academic or career goals. There is also a gap in personalized learning support during idle classroom hours. Institutions currently lack tools that integrate daily schedules with individual student planning and automated tracking.",
"relevant_stakeholders___beneficiaries": "• Students\n• Teachers\n• College administrators\n• Career counselors\n• Education departments",
"supporting_data": "• Surveys and reports on classroom time usage, student productivity, and NEP 2020 recommendations emphasizing personalized and experiential learning.",
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nMany educational institutions still depend on manual attendance systems, which are time-consuming and error-prone. Teachers spend a significant portion of class time marking attendance, reducing valuable instructional hours. Additionally, students often waste free periods with unproductive activities due to a lack of structured guidance. This leads to poor time management and reduced alignment with long-term academic or career goals. There is also a gap in personalized learning support during idle classroom hours. Institutions currently lack tools that integrate daily schedules with individual student planning and automated tracking.\n\nImpact / Why this problem needs to be solved\n\nThis issue impacts both administrative efficiency and student development. Automating attendance saves teachers' time and ensures more accurate records. Additionally, providing students with structured personal development activities during free time helps improve productivity, goal clarity, and learning outcomes. Institutions can also gain better insight into student behavior and engagement, allowing for more targeted support.\n\nExpected Outcomes\n\n• Automatically marks student attendance based on the daily timetable using QR code, Bluetooth/Wi-Fi proximity, or face recognition.\n• Displays real-time attendance on a classroom screen.\n• Suggests personalized academic tasks during free periods based on the student's interests, strengths, and career goals.\n• Generates a daily routine combining class schedule, free time, and long-term personal goals.\n\nThe app will require minimal infrastructure and be usable by both students and staff with basic training.\n\nRelevant Stakeholders / Beneficiaries\n\n• Students\n• Teachers\n• College administrators\n• Career counselors\n• Education departments\n\nSupporting Data\n\n• Surveys and reports on classroom time usage, student productivity, and NEP 2020 recommendations emphasizing personalized and experiential learning.",
"ps_number": "SIH25011",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Smart Curriculum Activity & Attendance App"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• A low-cost, user-friendly software or mobile application that automates attendance using facial recognition or RFID-based systems.\n• Requires minimal infrastructure and training for deployment in rural schools.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": "This issue affects over 50% of rural schools, impacting millions of students and teachers. It leads to inefficiencies, delays in reporting, and potential mismanagement of resources. Solving this will save time, improve accuracy, and enhance resource allocation.",
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "Many rural schools in India rely on manual attendance systems, which are time-consuming and prone to errors. Teachers spend significant time marking attendance, reducing instructional time. Additionally, inaccurate records can lead to discrepancies in government reporting for schemes like mid-day meals. This problem is prevalent in under-resourced schools with limited access to technology, affecting administrative efficiency and student tracking.",
"relevant_stakeholders___beneficiaries": "• School administrators\n• Teachers\n• Students\n• Government education departments",
"supporting_data": "• Annual Status of Education Report (ASER) 2024, highlighting administrative challenges in rural schools.",
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nMany rural schools in India rely on manual attendance systems, which are time-consuming and prone to errors. Teachers spend significant time marking attendance, reducing instructional time. Additionally, inaccurate records can lead to discrepancies in government reporting for schemes like mid-day meals. This problem is prevalent in under-resourced schools with limited access to technology, affecting administrative efficiency and student tracking.\n\nImpact / Why this problem needs to be solved\n\nThis issue affects over 50% of rural schools, impacting millions of students and teachers. It leads to inefficiencies, delays in reporting, and potential mismanagement of resources. Solving this will save time, improve accuracy, and enhance resource allocation.\n\nExpected Outcomes\n\n• A low-cost, user-friendly software or mobile application that automates attendance using facial recognition or RFID-based systems.\n• Requires minimal infrastructure and training for deployment in rural schools.\n\nRelevant Stakeholders / Beneficiaries\n\n• School administrators\n• Teachers\n• Students\n• Government education departments\n\nSupporting Data\n\n• Annual Status of Education Report (ASER) 2024, highlighting administrative challenges in rural schools.",
"ps_number": "SIH25012",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Automated Attendance System for Rural Schools"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• A mobile app or web platform integrating GPS-based real-time tracking of buses.\n• Display estimated arrival times and route information.\n• Optimized for low-bandwidth environments to ensure accessibility in smaller towns.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": "Over 60% of commuters in small cities face delays due to lack of real-time information, reducing public transport usage and increasing private vehicle dependency, which worsens traffic and pollution. A solution would enhance commuter experience and promote sustainable transport.",
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "In small cities and tier-2 towns, public transport systems lack real-time tracking, causing inconvenience to commuters who face unpredictable bus schedules. This leads to overcrowding, wasted time, and reduced reliance on public transport. The problem is acute in cities with growing populations but limited digital infrastructure for transport management.",
"relevant_stakeholders___beneficiaries": "• Commuters\n• Local transport authorities\n• Municipal corporations",
"supporting_data": "• Urban Mobility India Report 2024, emphasizing transport inefficiencies in tier-2 cities.",
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nIn small cities and tier-2 towns, public transport systems lack real-time tracking, causing inconvenience to commuters who face unpredictable bus schedules. This leads to overcrowding, wasted time, and reduced reliance on public transport. The problem is acute in cities with growing populations but limited digital infrastructure for transport management.\n\nImpact / Why this problem needs to be solved\n\nOver 60% of commuters in small cities face delays due to lack of real-time information, reducing public transport usage and increasing private vehicle dependency, which worsens traffic and pollution. A solution would enhance commuter experience and promote sustainable transport.\n\nExpected Outcomes\n\n• A mobile app or web platform integrating GPS-based real-time tracking of buses.\n• Display estimated arrival times and route information.\n• Optimized for low-bandwidth environments to ensure accessibility in smaller towns.\n\nRelevant Stakeholders / Beneficiaries\n\n• Commuters\n• Local transport authorities\n• Municipal corporations\n\nSupporting Data\n\n• Urban Mobility India Report 2024, emphasizing transport inefficiencies in tier-2 cities.",
"ps_number": "SIH25013",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Transportation & Logistics",
"title": "Real-Time Public Transport Tracking for Small Cities"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• IoT-based system or mobile app to monitor segregation at collection points.\n• Provide real-time feedback to households on compliance.\n• Generate compliance reports for local authorities to enforce better waste management.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": "Improper segregation affects nearly 70% of urban waste management systems, increasing recycling costs and environmental pollution. A solution would improve waste processing efficiency and reduce landfill dependency.",
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "Urban local bodies struggle with improper waste segregation at the household level, leading to inefficiencies in waste processing and increased landfill burden. Despite awareness campaigns, compliance remains low due to lack of monitoring and feedback mechanisms. This issue is widespread in urban areas with high waste generation.",
"relevant_stakeholders___beneficiaries": "• Urban local bodies\n• Residents\n• Waste management agencies\n• Environmental organizations",
"supporting_data": "Swachh Bharat Mission Urban 2.0 Report, highlighting segregation challenges.",
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nUrban local bodies struggle with improper waste segregation at the household level, leading to inefficiencies in waste processing and increased landfill burden. Despite awareness campaigns, compliance remains low due to lack of monitoring and feedback mechanisms. This issue is widespread in urban areas with high waste generation.\n\nImpact / Why this problem needs to be solved\n\nImproper segregation affects nearly 70% of urban waste management systems, increasing recycling costs and environmental pollution. A solution would improve waste processing efficiency and reduce landfill dependency.\n\nExpected Outcomes\n\n• IoT-based system or mobile app to monitor segregation at collection points.\n• Provide real-time feedback to households on compliance.\n• Generate compliance reports for local authorities to enforce better waste management.\n\nRelevant Stakeholders / Beneficiaries\n\n• Urban local bodies\n• Residents\n• Waste management agencies\n• Environmental organizations\n\nSupporting Data\n\nSwachh Bharat Mission Urban 2.0 Report, highlighting segregation challenges.",
"ps_number": "SIH25014",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Clean & Green Technology",
"title": "Waste Segregation Monitoring System for Urban Local Bodies"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• A smart automated system with sensors, cameras, and AI algorithms to detect infection levels.\n• IoT-controlled sprayer to dispense pesticides only where and when needed.\n• Mobile or web interface for farmers to monitor plant health and control the system remotely.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": "• Reduces excessive pesticide usage and lowers farming costs.\n• Protects soil and water quality, while minimizing harm to non-target organisms.\n• Improves crop yield and quality through precise and timely treatment.\n• Supports small and marginal farmers with cost savings and better farm sustainability.\n• Contributes to safe and eco-friendly food production.",
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "Excessive and indiscriminate application of pesticides in agriculture creates soil degradation, water contamination, damage to useful insects, and health risks for humans and animals. Regardless of whether the plant is healthy or infected, traditional pesticide spraying methods are used evenly, leading to waste and contamination. Many farmers lack affordable, automated systems to monitor crop health and control pesticide usage accordingly. This issue occurs on both large and small farms where manual inspections and sprays are labor-intensive, inefficient, and often inaccurate.\n\nAn intelligent system is required to recognize pest or disease infection in individual plants and regulate the amount of pesticide sprayed. This ensures optimal use of chemicals, reduces environmental impact, and promotes sustainable agriculture.",
"relevant_stakeholders___beneficiaries": "• Farmers (small, medium, and large scale)\n• Agricultural extension officers\n• Agrochemical companies\n• Environmental agencies\n• Consumers demanding residue-free produce\n• Government bodies promoting sustainable farming",
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nExcessive and indiscriminate application of pesticides in agriculture creates soil degradation, water contamination, damage to useful insects, and health risks for humans and animals. Regardless of whether the plant is healthy or infected, traditional pesticide spraying methods are used evenly, leading to waste and contamination. Many farmers lack affordable, automated systems to monitor crop health and control pesticide usage accordingly. This issue occurs on both large and small farms where manual inspections and sprays are labor-intensive, inefficient, and often inaccurate.\n\nAn intelligent system is required to recognize pest or disease infection in individual plants and regulate the amount of pesticide sprayed. This ensures optimal use of chemicals, reduces environmental impact, and promotes sustainable agriculture.\n\nImpact / Why this problem needs to be solved\n\n• Reduces excessive pesticide usage and lowers farming costs.\n• Protects soil and water quality, while minimizing harm to non-target organisms.\n• Improves crop yield and quality through precise and timely treatment.\n• Supports small and marginal farmers with cost savings and better farm sustainability.\n• Contributes to safe and eco-friendly food production.\n\nExpected Outcomes\n\n• A smart automated system with sensors, cameras, and AI algorithms to detect infection levels.\n• IoT-controlled sprayer to dispense pesticides only where and when needed.\n• Mobile or web interface for farmers to monitor plant health and control the system remotely.\n\nRelevant Stakeholders / Beneficiaries\n\n• Farmers (small, medium, and large scale)\n• Agricultural extension officers\n• Agrochemical companies\n• Environmental agencies\n• Consumers demanding residue-free produce\n• Government bodies promoting sustainable farming",
"ps_number": "SIH25015",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "Intelligent Pesticide Sprinkling System Determined by the Infection Level of a Plant"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• Automated attendance system using QR codes, biometrics, or facial recognition.\n• Cloud-based dashboard for administrators and faculty to review attendance records.\n• Analytics to identify attendance trends and student engagement levels.\n• Compatibility with both offline and online classes.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": "• Saves valuable teaching time otherwise wasted on manual attendance.\n• Reduces errors and eliminates the problem of proxy attendance.\n• Provides actionable insights for faculty to identify disengaged or struggling students.\n• Enhances transparency and accountability in academic processes.\n• Supports digital transformation of higher education institutions.",
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "Attendance tracking in most colleges is still done manually, usually through roll calls or paper registers. This consumes valuable teaching time and often leads to errors such as incorrect entries or proxy attendance. In larger classes, the issue becomes even harder to manage.\n\nAdditionally, faculty and administrators lack easy access to attendance insights, making it difficult to identify students at risk or to track patterns in engagement. As education undergoes digital transformation, continuing to rely on outdated systems creates unnecessary inefficiencies and delays.\n\nThere is a clear need for a solution that not only automates attendance but also provides analytics for better academic planning. Such a system should be user-friendly, reliable, and work seamlessly in both in-person and online settings.",
"relevant_stakeholders___beneficiaries": "• Students\n• Faculty and academic administrators\n• College management bodies\n• Education departments and policymakers",
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nAttendance tracking in most colleges is still done manually, usually through roll calls or paper registers. This consumes valuable teaching time and often leads to errors such as incorrect entries or proxy attendance. In larger classes, the issue becomes even harder to manage.\n\nAdditionally, faculty and administrators lack easy access to attendance insights, making it difficult to identify students at risk or to track patterns in engagement. As education undergoes digital transformation, continuing to rely on outdated systems creates unnecessary inefficiencies and delays.\n\nThere is a clear need for a solution that not only automates attendance but also provides analytics for better academic planning. Such a system should be user-friendly, reliable, and work seamlessly in both in-person and online settings.\n\nImpact / Why this problem needs to be solved\n\n• Saves valuable teaching time otherwise wasted on manual attendance.\n• Reduces errors and eliminates the problem of proxy attendance.\n• Provides actionable insights for faculty to identify disengaged or struggling students.\n• Enhances transparency and accountability in academic processes.\n• Supports digital transformation of higher education institutions.\n\nExpected Outcomes\n\n• Automated attendance system using QR codes, biometrics, or facial recognition.\n• Cloud-based dashboard for administrators and faculty to review attendance records.\n• Analytics to identify attendance trends and student engagement levels.\n• Compatibility with both offline and online classes.\n\nRelevant Stakeholders / Beneficiaries\n\n• Students\n• Faculty and academic administrators\n• College management bodies\n• Education departments and policymakers",
"ps_number": "SIH25016",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Automated Student Attendance Monitoring and Analytics System for Colleges"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• A centralized alumni management platform to store and update alumni data.\n• Features for communication, networking, and event management.\n• Secure system for tracking career progress, mentorship opportunities, and donations.\n• Easy-to-use interface for both administrators and alumni.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": "• Strengthens alumni engagement and builds long-term institutional relationships.\n• Provides opportunities for mentorship, internships, and collaborations.\n• Enhances fundraising potential through better alumni outreach.\n• Increases institutional credibility and community building.",
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "Most educational institutions do not have a reliable or centralized system to manage their alumni data. Once students graduate, their contact information, academic records, and career updates are often scattered across multiple platforms or lost entirely. In many cases, alumni communication is restricted to informal WhatsApp groups or outdated mailing lists, making long-term engagement difficult.\n\nThis lack of a structured system limits the potential of alumni relationships. Institutions miss opportunities to involve alumni in events, mentoring, internships, or fundraising. In a digitally connected world, the absence of a proper alumni management system creates a significant gap in outreach and growth.",
"relevant_stakeholders___beneficiaries": "• Alumni\n• Current students (through mentorship and internships)\n• Faculty and institution administrators\n• College/university management bodies\n• Employers and recruiters\nOrganization",
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nMost educational institutions do not have a reliable or centralized system to manage their alumni data. Once students graduate, their contact information, academic records, and career updates are often scattered across multiple platforms or lost entirely. In many cases, alumni communication is restricted to informal WhatsApp groups or outdated mailing lists, making long-term engagement difficult.\n\nThis lack of a structured system limits the potential of alumni relationships. Institutions miss opportunities to involve alumni in events, mentoring, internships, or fundraising. In a digitally connected world, the absence of a proper alumni management system creates a significant gap in outreach and growth.\n\nImpact / Why this problem needs to be solved\n\n• Strengthens alumni engagement and builds long-term institutional relationships.\n• Provides opportunities for mentorship, internships, and collaborations.\n• Enhances fundraising potential through better alumni outreach.\n• Increases institutional credibility and community building.\n\nExpected Outcomes\n\n• A centralized alumni management platform to store and update alumni data.\n• Features for communication, networking, and event management.\n• Secure system for tracking career progress, mentorship opportunities, and donations.\n• Easy-to-use interface for both administrators and alumni.\n\nRelevant Stakeholders / Beneficiaries\n\n• Alumni\n• Current students (through mentorship and internships)\n• Faculty and institution administrators\n• College/university management bodies\n• Employers and recruiters\nOrganization",
"ps_number": "SIH25017",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Digital Platform for Centralized Alumni Data Management and Engagement"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• A multilingual telemedicine app for video consultations with doctors.\n• Digital health records accessible offline for rural patients.\n• Real-time updates on medicine availability at local pharmacies.\n• AI-powered symptom checker optimized for low-bandwidth areas.\n• A scalable solution for other rural regions in India.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": "This problem directly affects the health and livelihood of thousands of rural residents, especially daily-wage earners and farmers. Lack of accessible healthcare leads to preventable complications, financial losses, and overall decline in community well-being. Addressing this issue would improve healthcare delivery, reduce unnecessary travel, and enhance quality of life for a large, underserved population.",
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "Nabha and its surrounding rural areas face significant healthcare challenges. The local Civil Hospital operates at less than 50% staff capacity, with only 11 doctors for 23 sanctioned posts. Patients from 173 villages travel long distances, often missing work, only to find that specialists are unavailable or medicines are out of stock. Poor road conditions and sanitation further hinder access. Many residents lack timely medical care, leading to worsened health outcomes and increased financial strain.",
"relevant_stakeholders___beneficiaries": "• Rural patients in Nabha and surrounding villages.\n• Nabha Civil Hospital staff.\n• Punjab Health Department.\n• Local pharmacies.\n• Daily-wage workers and farmers.",
"supporting_data": "• Nabha Civil Hospital serves 173 villages but has only 11 out of 23 sanctioned doctors.\n• Only 31% of rural Punjab households have internet access, highlighting the need for offline features.\n• Telemedicine adoption in India is growing at a 31% CAGR (2020–2025).\n• Sources: Local news reports and government health statistics.",
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nNabha and its surrounding rural areas face significant healthcare challenges. The local Civil Hospital operates at less than 50% staff capacity, with only 11 doctors for 23 sanctioned posts. Patients from 173 villages travel long distances, often missing work, only to find that specialists are unavailable or medicines are out of stock. Poor road conditions and sanitation further hinder access. Many residents lack timely medical care, leading to worsened health outcomes and increased financial strain.\n\nImpact / Why this problem needs to be solved\n\nThis problem directly affects the health and livelihood of thousands of rural residents, especially daily-wage earners and farmers. Lack of accessible healthcare leads to preventable complications, financial losses, and overall decline in community well-being. Addressing this issue would improve healthcare delivery, reduce unnecessary travel, and enhance quality of life for a large, underserved population.\n\nExpected Outcomes\n\n• A multilingual telemedicine app for video consultations with doctors.\n• Digital health records accessible offline for rural patients.\n• Real-time updates on medicine availability at local pharmacies.\n• AI-powered symptom checker optimized for low-bandwidth areas.\n• A scalable solution for other rural regions in India.\n\nRelevant Stakeholders / Beneficiaries\n\n• Rural patients in Nabha and surrounding villages.\n• Nabha Civil Hospital staff.\n• Punjab Health Department.\n• Local pharmacies.\n• Daily-wage workers and farmers.\n\nSupporting Data\n\n• Nabha Civil Hospital serves 173 villages but has only 11 out of 23 sanctioned doctors.\n• Only 31% of rural Punjab households have internet access, highlighting the need for offline features.\n• Telemedicine adoption in India is growing at a 31% CAGR (2020–2025).\n• Sources: Local news reports and government health statistics.",
"ps_number": "SIH25018",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "MedTech / BioTech / HealthTech",
"title": "Telemedicine Access for Rural Healthcare in Nabha"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": "• A mobile and web-based digital learning app that works offline.\n• Interactive lessons in local languages to improve engagement.\n• Digital literacy modules tailored for rural students.\n• Teacher dashboards to track student progress.\n• Optimized for low-end devices and poor connectivity.",
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": "The lack of digital resources and skills limits students’ academic growth and future employability. With the increasing importance of digital literacy, students in rural Nabha risk being left behind, perpetuating cycles of educational and economic disadvantage. Addressing this problem is urgent to ensure equitable access to quality education and to empower rural youth with skills for the modern world.",
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "Many government schools in Nabha and nearby rural areas lack updated computer infrastructure, reliable internet connectivity, and access to quality digital educational resources. Teachers and students struggle to use outdated systems, and digital literacy remains low. As a result, students face difficulties in learning essential digital skills and accessing modern educational content, leading to a widening gap between rural and urban education standards.",
"relevant_stakeholders___beneficiaries": "• Rural school students and teachers in Nabha.\n• School administrators.\n• Parents.\n• Punjab Education Department.",
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Punjab",
"problem_description": "Problem Description\n\nMany government schools in Nabha and nearby rural areas lack updated computer infrastructure, reliable internet connectivity, and access to quality digital educational resources. Teachers and students struggle to use outdated systems, and digital literacy remains low. As a result, students face difficulties in learning essential digital skills and accessing modern educational content, leading to a widening gap between rural and urban education standards.\n\nImpact / Why this problem needs to be solved\n\nThe lack of digital resources and skills limits students’ academic growth and future employability. With the increasing importance of digital literacy, students in rural Nabha risk being left behind, perpetuating cycles of educational and economic disadvantage. Addressing this problem is urgent to ensure equitable access to quality education and to empower rural youth with skills for the modern world.\n\nExpected Outcomes\n\n• A mobile and web-based digital learning app that works offline.\n• Interactive lessons in local languages to improve engagement.\n• Digital literacy modules tailored for rural students.\n• Teacher dashboards to track student progress.\n• Optimized for low-end devices and poor connectivity.\n\nRelevant Stakeholders / Beneficiaries\n\n• Rural school students and teachers in Nabha.\n• School administrators.\n• Parents.\n• Punjab Education Department.",
"ps_number": "SIH25019",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Digital Learning Platform for Rural School Students in Nabha"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "Indian Railways employs Track Recording Cars (TRCs) for periodic assessment of track geometry to ensure safety, ride comfort, and operational efficiency. While some TRCs still use outdated, contact-based systems, others rely on imported contactless technologies—typically laser-based solutions integrated with proprietary software.\n\nThese foreign systems pose several challenges:\n• High procurement and maintenance costs\n• Limited customization capabilities\n• Dependency on external vendors and closed-source software\n\nTo address these issues and promote self-reliance, Indian Railways invites proposals for the Development of indigenous contactless Integrated Track Monitoring Systems (ITMS) for Track Recording on Indian Railways.",
"conclusion": "This initiative is a step toward achieving technological independence in railway infrastructure monitoring. By fostering domestic innovation, Indian Railways aims to reduce reliance on foreign systems, cut long-term costs, and deploy tailored solutions better suited to India’s operational context.",
"data_privacy_&_security": null,
"deliverables": "Successful participants must submit:\n• A fully functional prototype (hardware + software)\n• Comprehensive technical documentation\n• Validation report demonstrating standards compliance\n• Video demonstration (lab/field performance)\n• Cost analysis and scalability roadmap",
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": "Open to:\n• Startups\n• Entrepreneurs\n• Academic & research institutions\n• Industry professionals and R&D organizations\n• Collaborative consortia involving any of the above",
"evaluation_criteria": "• Technical feasibility and innovation – 10 marks\n• Standards compliance (EN 13848 & RDSO TM/IM/448, Rev. 1:2023) – 40 marks\n• Hardware robustness, modularity & compactness – 20 marks\n• Software usability & architecture – 10 marks\n• Scalability, maintainability & upgradability – 10 marks\n• Cost-effectiveness – 10 marks\nTotal = 100 marks",
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": "• Maximum Speed: 200 km/h\n• Sampling Rate: 25 cm\n• Accuracy: As per EN 13848 Part 2 & RDSO TM/IM/448, Rev. 1: 2023\n• Real-time Processing: Required\n• Chainage Mapping: Mandatory (via axle encoder)",
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": "Design and develop a comprehensive, indigenous Integrated Track Monitoring System that:\n• Fully complies with RDSO Specification TM/IM/448, Rev. 1: 2023 and EN 13848 standards.\n• Supports modularity, ease of maintenance, and cost efficiency.",
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": "Sub-systems:\n• Track geometry measurement system\n• Full rail profile and wear measurement system\n• Condition monitoring of track components\n• Acceleration measurement system\n• Infringement measurement to MMD/SOD\n• Rear window video recording system\n\nData Acquisition & Sampling:\n• Sampling interval: 25 cm\n• Operable under dynamic loads at speeds from 0–200 km/h\n• Real-time acquisition and processing capability\n\nData Processing & Analysis:\n• Onboard server for real-time data filtering and analysis\n• Software must:\n– Eliminate environmental and operational noise\n– Extract and analyze track geometry parameters & track component conditions\n– Comply with EN 13848 Part 1 & 2 and RDSO TM/IM/448, Rev. 1: 2023 accuracy standards\n\nOutput & Storage:\n• Chainage-mapped outputs for:\n– Track Geometry: Gauge, alignment, unevenness, twist, cross level, curve\n– Dynamic Parameters: Vertical & lateral acceleration\n– Rail Condition: Profile and wear\n– Track Components: Rail surface, fastenings, ballast, sleepers\n– Synchronized rear-view video footage\n– Exportable data in standard formats (CSV, XML, JPEG, AVI)\n• Secure storage and archival capability",
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Railways",
"problem_description": "Background\n\nIndian Railways employs Track Recording Cars (TRCs) for periodic assessment of track geometry to ensure safety, ride comfort, and operational efficiency. While some TRCs still use outdated, contact-based systems, others rely on imported contactless technologies—typically laser-based solutions integrated with proprietary software.\n\nThese foreign systems pose several challenges:\n• High procurement and maintenance costs\n• Limited customization capabilities\n• Dependency on external vendors and closed-source software\n\nTo address these issues and promote self-reliance, Indian Railways invites proposals for the Development of indigenous contactless Integrated Track Monitoring Systems (ITMS) for Track Recording on Indian Railways.\n\nObjective\n\nDesign and develop a comprehensive, indigenous Integrated Track Monitoring System that:\n• Fully complies with RDSO Specification TM/IM/448, Rev. 1: 2023 and EN 13848 standards.\n• Supports modularity, ease of maintenance, and cost efficiency.\n\nTechnical Scope\n\nSub-systems:\n• Track geometry measurement system\n• Full rail profile and wear measurement system\n• Condition monitoring of track components\n• Acceleration measurement system\n• Infringement measurement to MMD/SOD\n• Rear window video recording system\n\nData Acquisition & Sampling:\n• Sampling interval: 25 cm\n• Operable under dynamic loads at speeds from 0–200 km/h\n• Real-time acquisition and processing capability\n\nData Processing & Analysis:\n• Onboard server for real-time data filtering and analysis\n• Software must:\n– Eliminate environmental and operational noise\n– Extract and analyze track geometry parameters & track component conditions\n– Comply with EN 13848 Part 1 & 2 and RDSO TM/IM/448, Rev. 1: 2023 accuracy standards\n\nOutput & Storage:\n• Chainage-mapped outputs for:\n– Track Geometry: Gauge, alignment, unevenness, twist, cross level, curve\n– Dynamic Parameters: Vertical & lateral acceleration\n– Rail Condition: Profile and wear\n– Track Components: Rail surface, fastenings, ballast, sleepers\n– Synchronized rear-view video footage\n– Exportable data in standard formats (CSV, XML, JPEG, AVI)\n• Secure storage and archival capability\n\nKey Performance Parameters\n\n• Maximum Speed: 200 km/h\n• Sampling Rate: 25 cm\n• Accuracy: As per EN 13848 Part 2 & RDSO TM/IM/448, Rev. 1: 2023\n• Real-time Processing: Required\n• Chainage Mapping: Mandatory (via axle encoder)\n\nEligibility\n\nOpen to:\n• Startups\n• Entrepreneurs\n• Academic & research institutions\n• Industry professionals and R&D organizations\n• Collaborative consortia involving any of the above\n\nEvaluation Criteria\n\n• Technical feasibility and innovation – 10 marks\n• Standards compliance (EN 13848 & RDSO TM/IM/448, Rev. 1:2023) – 40 marks\n• Hardware robustness, modularity & compactness – 20 marks\n• Software usability & architecture – 10 marks\n• Scalability, maintainability & upgradability – 10 marks\n• Cost-effectiveness – 10 marks\nTotal = 100 marks\n\nDeliverables\n\nSuccessful participants must submit:\n• A fully functional prototype (hardware + software)\n• Comprehensive technical documentation\n• Validation report demonstrating standards compliance\n• Video demonstration (lab/field performance)\n• Cost analysis and scalability roadmap\n\nConclusion\n\nThis initiative is a step toward achieving technological independence in railway infrastructure monitoring. By fostering domestic innovation, Indian Railways aims to reduce reliance on foreign systems, cut long-term costs, and deploy tailored solutions better suited to India’s operational context.",
"ps_number": "SIH25020",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "Development of indigenous contactless Integrated Track Monitoring Systems (ITMS) for Track Recording on Indian Railways"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "Indian Railways procures about 10 crore Elastic Rail Clips, 5 crore liners, and 8.5 crore rail pads annually. There is currently no system for identification of these track fittings—i.e., elastic rail clips, rail pads, liners—and sleepers, with integration to the UDM portal enabling mobile-based scanning for vendor lot number, date of supply, warranty period, inspection dates, etc. This gap is critical for quality assessment and performance management of fittings.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "This problem statement envisages a unified system for laser-based QR code marking on track fittings to enable mobile scanning and identification of elastic rail clips, rail pads, liners, and sleepers, along with integration to the UDM (User Depot Module) on www.ireps.gov.in and the TMS (Track Management System) on www.irecept.gov.in. With the use of AI, the system should extract all essential details of each fitting item, including inspections at all stages—from manufacturing and supply to in-service performance—and pinpoint exceptions for quality monitoring. Laser-based QR codes are already prevalent in other industries.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "Innovative solutions are invited through Smart India Hackathon 2025 to develop and implement a system addressing identification of bulk supply materials, their performance issues, and effective inventory management and quality monitoring actions for safety performance of fittings.\n\n• Hardware Solution: Design and implement laser-based QR code imprints on bulk supply items of track fittings and sleepers.\n• Software Solution: Develop QR code linkage and integrate with the UDM and TMS portals; enable mobile scans to generate AI-based reports related to vendor, supply, warranty, inspections, and support inventory management, etc.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Railways",
"problem_description": "Background\n\nIndian Railways procures about 10 crore Elastic Rail Clips, 5 crore liners, and 8.5 crore rail pads annually. There is currently no system for identification of these track fittings—i.e., elastic rail clips, rail pads, liners—and sleepers, with integration to the UDM portal enabling mobile-based scanning for vendor lot number, date of supply, warranty period, inspection dates, etc. This gap is critical for quality assessment and performance management of fittings.\n\nDescription\n\nThis problem statement envisages a unified system for laser-based QR code marking on track fittings to enable mobile scanning and identification of elastic rail clips, rail pads, liners, and sleepers, along with integration to the UDM (User Depot Module) on www.ireps.gov.in and the TMS (Track Management System) on www.irecept.gov.in. With the use of AI, the system should extract all essential details of each fitting item, including inspections at all stages—from manufacturing and supply to in-service performance—and pinpoint exceptions for quality monitoring. Laser-based QR codes are already prevalent in other industries.\n\nExpected Solution\n\nInnovative solutions are invited through Smart India Hackathon 2025 to develop and implement a system addressing identification of bulk supply materials, their performance issues, and effective inventory management and quality monitoring actions for safety performance of fittings.\n\n• Hardware Solution: Design and implement laser-based QR code imprints on bulk supply items of track fittings and sleepers.\n• Software Solution: Develop QR code linkage and integrate with the UDM and TMS portals; enable mobile scans to generate AI-based reports related to vendor, supply, warranty, inspections, and support inventory management, etc.",
"ps_number": "SIH25021",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Transportation & Logistics",
"title": "AI based development of Laser based QR Code marking on track fittings on Indian Railways"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Indian Railways manages train movements primarily through the experience of train traffic controllers. While effective, this manual approach faces limitations as network congestion and operational complexity grow. Trains of varying types and priorities must share limited track infrastructure across space and time, making optimal allocation a significant challenge. The problem is a large-scale combinatorial optimization task with numerous constraints such as safety, track resources, system of working, signalling system, platform availability, train schedules, and train priorities. As real-time decisions become increasingly complex, there is a growing need for intelligent, data-driven systems powered by optimization algorithms and AI to enhance efficiency, punctuality, and utilization of railway infrastructure.\n\nDetailed Description\n\nCurrently, experienced traffic controllers oversee operations and take real-time decisions—whether a train should proceed, halt, or be rerouted—based on operational conditions and institutional knowledge. With rising traffic volumes and higher expectations for punctuality, safety, and efficiency, manual decision-making alone is becoming insufficient.\n\nThe network is constrained by finite infrastructure—limited track sections, junctions, crossings, and platform capacities—shared by long-distance express, suburban local, freight, maintenance blocks, and unscheduled specials. Coordinating these movements across spatial (network layout) and temporal (scheduling) dimensions while maintaining safety and minimizing delays is formidable.\n\nWithin a section managed by a section controller, the core problem is to decide train precedence and crossings to maximize throughput and minimize overall train travel time, considering section characteristics (e.g., line capacity, gradients, signal placements) and varying train priorities. This represents a dynamic, large-scale combinatorial optimization problem with an exponentially large solution space, further complicated by real-time disruptions (breakdowns, weather, rolling stock delays). Human intuition alone is no longer sufficient; intelligent decision-support tools are required to improve precision, scalability, and responsiveness.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "An intelligent decision-support system that assists section controllers in making optimized, real-time decisions for train precedence and crossings. The system should:\n\n• Leverage operations research and AI to model constraints, train priorities, and operational rules, producing conflict-free, feasible schedules dynamically.\n• Maximize section throughput and minimize overall train travel time, with the ability to re-optimize rapidly under disruptions (e.g., incidents, delays, weather).\n• Support what-if simulation and scenario analysis to evaluate alternative routings, holding strategies, and platform allocations.\n• Provide a user-friendly interface for controllers with clear recommendations, explanations, and override capabilities.\n• Integrate with existing railway control systems and data sources (signalling, TMS, timetables, rolling stock status) via secure APIs.\n• Include audit trails, performance dashboards, and KPIs (punctuality, average delay, throughput, utilization) for continuous improvement.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
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"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Railways",
"problem_description": "Background\n\nIndian Railways manages train movements primarily through the experience of train traffic controllers. While effective, this manual approach faces limitations as network congestion and operational complexity grow. Trains of varying types and priorities must share limited track infrastructure across space and time, making optimal allocation a significant challenge. The problem is a large-scale combinatorial optimization task with numerous constraints such as safety, track resources, system of working, signalling system, platform availability, train schedules, and train priorities. As real-time decisions become increasingly complex, there is a growing need for intelligent, data-driven systems powered by optimization algorithms and AI to enhance efficiency, punctuality, and utilization of railway infrastructure.\n\nDetailed Description\n\nCurrently, experienced traffic controllers oversee operations and take real-time decisions—whether a train should proceed, halt, or be rerouted—based on operational conditions and institutional knowledge. With rising traffic volumes and higher expectations for punctuality, safety, and efficiency, manual decision-making alone is becoming insufficient.\n\nThe network is constrained by finite infrastructure—limited track sections, junctions, crossings, and platform capacities—shared by long-distance express, suburban local, freight, maintenance blocks, and unscheduled specials. Coordinating these movements across spatial (network layout) and temporal (scheduling) dimensions while maintaining safety and minimizing delays is formidable.\n\nWithin a section managed by a section controller, the core problem is to decide train precedence and crossings to maximize throughput and minimize overall train travel time, considering section characteristics (e.g., line capacity, gradients, signal placements) and varying train priorities. This represents a dynamic, large-scale combinatorial optimization problem with an exponentially large solution space, further complicated by real-time disruptions (breakdowns, weather, rolling stock delays). Human intuition alone is no longer sufficient; intelligent decision-support tools are required to improve precision, scalability, and responsiveness.\n\nExpected Solution\n\nAn intelligent decision-support system that assists section controllers in making optimized, real-time decisions for train precedence and crossings. The system should:\n\n• Leverage operations research and AI to model constraints, train priorities, and operational rules, producing conflict-free, feasible schedules dynamically.\n• Maximize section throughput and minimize overall train travel time, with the ability to re-optimize rapidly under disruptions (e.g., incidents, delays, weather).\n• Support what-if simulation and scenario analysis to evaluate alternative routings, holding strategies, and platform allocations.\n• Provide a user-friendly interface for controllers with clear recommendations, explanations, and override capabilities.\n• Integrate with existing railway control systems and data sources (signalling, TMS, timetables, rolling stock status) via secure APIs.\n• Include audit trails, performance dashboards, and KPIs (punctuality, average delay, throughput, utilization) for continuous improvement.",
"ps_number": "SIH25022",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Transportation & Logistics",
"title": "Maximizing Section Throughput Using AI-Powered Precise Train Traffic Control"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
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"impact": null,
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"introduction": "Background:- Panchakarma is gaining global recognition for detoxification, rejuvenation, and chronic disease management, contributing significantly to the projected USD 16 billion Ayurveda market by 2026. However, the lack of dedicated management software leads to: -Inefficient manual scheduling and documentation -Inconsistent therapy quality across centers -Limited digital patient management Advancements in healthcare IT and digital therapeutics present a timely opportunity to develop a software solution that integrates traditional authenticity with modern efficiency. Description:- The Panchakarma Management Software will feature automated therapy scheduling and provide notifications to patients regarding the necessary pre- and post-procedure precautions. The software will include: -Automated therapy scheduling system – to plan and manage therapy sessions automatically. -Notification system – to alert patients about pre-procedure and post-procedure precautions they need to follow. Innovative Features:- -Real-Time Therapy Tracking: Allow patients and practitioners to view therapy progress, upcoming sessions and personalized recovery milestones. -Visualization Tools: Use graphs and progress bars to track improvements based on patient responses and feedback. -Integrated Feedback Loop: Enable patients to report symptoms, side effects or improvements after each session, refining schedules or precautions as needed. Expected solution:- The software should address both core functionality and innovative enhancements to ensure usability, efficiency and patient-centric care. Platform should contain:- •The platform should have automated therapy scheduling Feature for both patients and practitioners to schedule, modify, and view upcoming therapy sessions. Pre- and Post-Procedure Notification System:- •Automated alerts and reminders to patients regarding the necessary precautions before and after procedures. •Customizable notification channels (in-app, SMS, email)",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Ayush",
"problem_description": "Background:- Panchakarma is gaining global recognition for detoxification, rejuvenation, and chronic disease management, contributing significantly to the projected USD 16 billion Ayurveda market by 2026. However, the lack of dedicated management software leads to: -Inefficient manual scheduling and documentation -Inconsistent therapy quality across centers -Limited digital patient management Advancements in healthcare IT and digital therapeutics present a timely opportunity to develop a software solution that integrates traditional authenticity with modern efficiency. Description:- The Panchakarma Management Software will feature automated therapy scheduling and provide notifications to patients regarding the necessary pre- and post-procedure precautions. The software will include: -Automated therapy scheduling system – to plan and manage therapy sessions automatically. -Notification system – to alert patients about pre-procedure and post-procedure precautions they need to follow. Innovative Features:- -Real-Time Therapy Tracking: Allow patients and practitioners to view therapy progress, upcoming sessions and personalized recovery milestones. -Visualization Tools: Use graphs and progress bars to track improvements based on patient responses and feedback. -Integrated Feedback Loop: Enable patients to report symptoms, side effects or improvements after each session, refining schedules or precautions as needed. Expected solution:- The software should address both core functionality and innovative enhancements to ensure usability, efficiency and patient-centric care. Platform should contain:- •The platform should have automated therapy scheduling Feature for both patients and practitioners to schedule, modify, and view upcoming therapy sessions. Pre- and Post-Procedure Notification System:- •Automated alerts and reminders to patients regarding the necessary precautions before and after procedures. •Customizable notification channels (in-app, SMS, email)",
"ps_number": "SIH25023",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "MedTech / BioTech / HealthTech",
"title": "AyurSutra- Panchakarma Patient Management and therapy scheduling Software"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Currently, in Ayurvedic hospitals, diet charts are prescribed manually by doctors in handwritten form, tailored to each patient’s needs. Existing software solutions primarily focus on macro- and micro-nutrient tracking but fail to align with Ayurvedic nutritional concepts. This gap creates inefficiencies, reduces accuracy, and makes it harder for practitioners to deliver holistic dietary care rooted in Ayurveda.\n\nDetailed Description\n\nThe problem envisages the development of a dedicated Ayurvedic Diet Management Software designed to efficiently create, manage, and organize patient-specific diet charts with both accuracy and ease. Unlike conventional nutrition tools, the platform will integrate modern nutritional metrics with Ayurvedic dietary principles—such as caloric value, food properties (Hot/Cold, Easy/Difficult to digest), and the six tastes (Rasa).",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "The proposed solution should provide an intuitive platform tailored for Ayurvedic dietitians, enabling quick food input, comprehensive nutrient tracking, and Ayurvedic dietary categorization.\n\nKey Features:\n• Scientifically calculated nutrient data for diverse food categories, customized for men, women, and children across all age groups.\n• A dynamic food database of 8,000+ items covering Indian, multicultural, and international cuisines for wide applicability.\n• Automated diet chart generation with nutritionally balanced, Ayurveda-compliant plans in a clear, organized format.\n• Comprehensive patient management module, including profiles with age, gender, dietary habits, meal frequency, bowel movements, water intake, and other critical health parameters.\n• Recipe-based diet charts with automated nutrient analysis to provide detailed, actionable guidance for patients.\n\nAdditional Features:\n• Security & Compliance: Ensure patient data privacy, adhering to health data regulations (e.g., HIPAA or local laws).\n• User Experience (UX): A clean, user-friendly interface with customization to match Ayurvedic practitioners’ workflows.\n• Integration Potential: Capability to integrate with hospital information systems (HIS) or electronic health records (EHR).\n• Mobile Support: Compatibility with mobile and tablet devices for on-the-go usage by doctors and patients.\n• Reporting Tools: Ability to generate printable diet charts and reports for patient handouts.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
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"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Ayush",
"problem_description": "Background\n\nCurrently, in Ayurvedic hospitals, diet charts are prescribed manually by doctors in handwritten form, tailored to each patient’s needs. Existing software solutions primarily focus on macro- and micro-nutrient tracking but fail to align with Ayurvedic nutritional concepts. This gap creates inefficiencies, reduces accuracy, and makes it harder for practitioners to deliver holistic dietary care rooted in Ayurveda.\n\nDetailed Description\n\nThe problem envisages the development of a dedicated Ayurvedic Diet Management Software designed to efficiently create, manage, and organize patient-specific diet charts with both accuracy and ease. Unlike conventional nutrition tools, the platform will integrate modern nutritional metrics with Ayurvedic dietary principles—such as caloric value, food properties (Hot/Cold, Easy/Difficult to digest), and the six tastes (Rasa).\n\nExpected Solution\n\nThe proposed solution should provide an intuitive platform tailored for Ayurvedic dietitians, enabling quick food input, comprehensive nutrient tracking, and Ayurvedic dietary categorization.\n\nKey Features:\n• Scientifically calculated nutrient data for diverse food categories, customized for men, women, and children across all age groups.\n• A dynamic food database of 8,000+ items covering Indian, multicultural, and international cuisines for wide applicability.\n• Automated diet chart generation with nutritionally balanced, Ayurveda-compliant plans in a clear, organized format.\n• Comprehensive patient management module, including profiles with age, gender, dietary habits, meal frequency, bowel movements, water intake, and other critical health parameters.\n• Recipe-based diet charts with automated nutrient analysis to provide detailed, actionable guidance for patients.\n\nAdditional Features:\n• Security & Compliance: Ensure patient data privacy, adhering to health data regulations (e.g., HIPAA or local laws).\n• User Experience (UX): A clean, user-friendly interface with customization to match Ayurvedic practitioners’ workflows.\n• Integration Potential: Capability to integrate with hospital information systems (HIS) or electronic health records (EHR).\n• Mobile Support: Compatibility with mobile and tablet devices for on-the-go usage by doctors and patients.\n• Reporting Tools: Ability to generate printable diet charts and reports for patient handouts.",
"ps_number": "SIH25024",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "MedTech / BioTech / HealthTech",
"title": "Comprehensive Cloud-Based Practice Management & Nutrient Analysis Software for Ayurvedic Dietitians Tailored for Ayurveda-Focused Diet Plans"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "In Ayurveda and other traditional medicine systems, Rasa (taste) is a fundamental criterion for identifying, classifying, and determining the therapeutic value of medicinal herbs. While this taste-based assessment has been effective for centuries, it is inherently subjective, relying on human perception and expert experience, which often leads to variability and inconsistency.\n\nIn the modern context, ensuring the authenticity, quality, and standardization of herbal raw materials is a critical challenge due to widespread adulteration, batch-to-batch phytochemical variations, and the global demand for reliable herbal products.\n\nRecent advances in sensor science and analytical technologies provide innovative solutions to these challenges. Electronic tongue (e-tongue) systems can mimic human gustatory mechanisms by detecting complex chemical signatures, while spectroscopic techniques such as Near-Infrared (NIR), Raman, and UV-Visible spectroscopy offer rapid, non-destructive chemical profiling of herbal samples.\n\nWhen integrated with Artificial Intelligence (AI) and Machine Learning (ML)—such as chemometric modelling, neural networks, and pattern recognition—large datasets can be analyzed to classify herbs based on taste profiles, threshold levels, and phytochemical composition with high precision. This AI-enabled, sensor-integrated approach ensures faster, objective, and cost-effective quality assurance, bridging traditional Ayurvedic principles with modern scientific validation.\n\nDetailed Description\n\nThe problem statement aims to design and develop a sensor-integrated AI-enabled device for objective quality assessment of marketed herbal samples. The device will incorporate:\n\n• A multi-sensor e-tongue array to detect basic taste modalities (sweet, sour, salty, bitter, pungent, astringent).\n• A threshold detection module for quantifying the minimum perceptible concentration of each taste-related compound.\n• Analytical integration with phytochemical profiling techniques (e.g., HPTLC, FTIR, LC-MS) for correlating taste signatures with chemical constituents.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "• A portable hardware prototype incorporating an electronic tongue (e-tongue) sensor array.\n• Calibration using authenticated herb samples and machine learning models trained on large datasets of taste and phytochemical fingerprints.\n• Smart alerts and decision support for detecting adulteration or substandard quality.\n• A centralized or cloud-compatible database that updates continuously with new samples and learning outputs.\n• Applicability across industries including Ayurvedic pharmacopeia standardization, herbal industry quality control, academic research, and regulatory bodies.",
"impact": null,
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"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
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"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Ayush",
"problem_description": "Background\n\nIn Ayurveda and other traditional medicine systems, Rasa (taste) is a fundamental criterion for identifying, classifying, and determining the therapeutic value of medicinal herbs. While this taste-based assessment has been effective for centuries, it is inherently subjective, relying on human perception and expert experience, which often leads to variability and inconsistency.\n\nIn the modern context, ensuring the authenticity, quality, and standardization of herbal raw materials is a critical challenge due to widespread adulteration, batch-to-batch phytochemical variations, and the global demand for reliable herbal products.\n\nRecent advances in sensor science and analytical technologies provide innovative solutions to these challenges. Electronic tongue (e-tongue) systems can mimic human gustatory mechanisms by detecting complex chemical signatures, while spectroscopic techniques such as Near-Infrared (NIR), Raman, and UV-Visible spectroscopy offer rapid, non-destructive chemical profiling of herbal samples.\n\nWhen integrated with Artificial Intelligence (AI) and Machine Learning (ML)—such as chemometric modelling, neural networks, and pattern recognition—large datasets can be analyzed to classify herbs based on taste profiles, threshold levels, and phytochemical composition with high precision. This AI-enabled, sensor-integrated approach ensures faster, objective, and cost-effective quality assurance, bridging traditional Ayurvedic principles with modern scientific validation.\n\nDetailed Description\n\nThe problem statement aims to design and develop a sensor-integrated AI-enabled device for objective quality assessment of marketed herbal samples. The device will incorporate:\n\n• A multi-sensor e-tongue array to detect basic taste modalities (sweet, sour, salty, bitter, pungent, astringent).\n• A threshold detection module for quantifying the minimum perceptible concentration of each taste-related compound.\n• Analytical integration with phytochemical profiling techniques (e.g., HPTLC, FTIR, LC-MS) for correlating taste signatures with chemical constituents.\n\nExpected Solution\n\n• A portable hardware prototype incorporating an electronic tongue (e-tongue) sensor array.\n• Calibration using authenticated herb samples and machine learning models trained on large datasets of taste and phytochemical fingerprints.\n• Smart alerts and decision support for detecting adulteration or substandard quality.\n• A centralized or cloud-compatible database that updates continuously with new samples and learning outputs.\n• Applicability across industries including Ayurvedic pharmacopeia standardization, herbal industry quality control, academic research, and regulatory bodies.",
"ps_number": "SIH25025",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "MedTech / BioTech / HealthTech",
"title": "E tongue for Dravya identification"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "India’s Ayush sector is rapidly transitioning from paper-based records to interoperable digital health systems. Central to this transformation are two key coding systems: the National AYUSH Morbidity & Standardized Terminologies Electronic (NAMASTE) codes, which provide over 4,500 standardized terms for Ayurveda, Siddha and Unani disorders, WHO Standardised International Terminologies for Ayurveda and the WHO’s ICD-11, Chapter – 26, Traditional Medicine Module 2 (TM2), which integrates 529 disorder categories and 196 pattern codes into the global ICD framework. Harmonising these vocabularies within Electronic Medical Record (EMR) platforms not only enables accurate clinical documentation and decision support but also ensures compliance with India’s 2016 EHR Standards—mandating FHIR R4 APIs, SNOMED CT and LOINC semantics, ISO 22600 access control, ABHA-linked OAuth 2.0 authentication, and robust audit trails for consent and versioning.\n\nTo operationalize this dual-coding approach, EMR vendors must implement a lightweight terminology micro-service that ingests NAMASTE CSV and synchronises with the WHO-11 ICD-API (Including Biomedicine and TM2). Within the EMR user interface, diagnosis entries should support auto-complete widgets that return both NAMASTE and ICD-11 (TM2 and Biomedicine) codes, comply with the Coding rules of ICD-11 framework and store them together in the patient’s Problem List resource. This integration empowers clinicians to combine traditional and biomedical insights, facilitates Ayush insurance claims under global ICD-11 coding rules, and provides the Ministry of Ayush with real-time morbidity analytics aligned with national and international reporting standards.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Design and prototype an API integration that brings India’s NAMASTE terminologies, WHO Standardised International terminology and the WHO ICD-11 (Traditional Medicine Module 2 (TM2) & Biomedicine) into a FHIR-compliant Electronic Medical Record (EMR) system. Your goal is to enable clinicians to record traditional medicine diagnoses (Ayurveda, Siddha, Unani) using NAMASTE codes, then automatically map them to global ICD-11 (TM2 and Biomedicine) identifiers—supporting dual/double-coding (One code denoting Ayurveda/Siddha/Unani (TM) and another denoting Biomedicine) for interoperability, analytics and insurance claims.\n\nYour deliverable is a lightweight micro-service or FHIR terminology plugin offering:\n• A FHIR compliant resource for NAMASTE codes linking to WHO International Terminologies of Ayurveda, ICD-11 (TM2 and Biomedicine) and compliant with ICD-11 Coding rules.\n• A REST endpoint for auto-complete value-set lookup.\n• A translation operation converting NAMASTE ↔ TM2 codes.\n• An encounter upload endpoint that ingests FHIR Bundles with both code systems.\n• OAuth 2.0–secured access using ABHA tokens and audit-ready metadata.\n\nTeams should demonstrate\n\n1. Ingesting the NAMASTE CSV export and generating FHIR CodeSystem + ConceptMap.\n2. Fetching TM2, Biomedicine updates from the WHO ICD-API and merging into your service.\n3. A simple web or CLI interface to search NAMASTE terms, WHO International Terminologies of Ayurveda, see mapped TM2 codes, and construct a FHIR ProblemList entry.\n4. Version tracking and consent metadata to satisfy India’s 2016 EHR Standards (FHIR R4, ISO 22600, SNOMED-CT/LOINC semantics).",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "A lightweight, FHIR R4–compliant terminology micro-service—built to India’s 2016 EHR Standards—that exposes a NAMASTE CodeSystem, WHO International Terminologies of Ayurveda, an ICD-11 TM2, Biomedicine ConceptMap, an auto-complete value-set lookup endpoint, a NAMASTE↔TM2 translate operation; ICD-11 Biomedicine look up and a secure FHIR Bundle upload interface (for enabling double coding).",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
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},
"organization": "Ministry of Ayush",
"problem_description": "Background\n\nIndia’s Ayush sector is rapidly transitioning from paper-based records to interoperable digital health systems. Central to this transformation are two key coding systems: the National AYUSH Morbidity & Standardized Terminologies Electronic (NAMASTE) codes, which provide over 4,500 standardized terms for Ayurveda, Siddha and Unani disorders, WHO Standardised International Terminologies for Ayurveda and the WHO’s ICD-11, Chapter – 26, Traditional Medicine Module 2 (TM2), which integrates 529 disorder categories and 196 pattern codes into the global ICD framework. Harmonising these vocabularies within Electronic Medical Record (EMR) platforms not only enables accurate clinical documentation and decision support but also ensures compliance with India’s 2016 EHR Standards—mandating FHIR R4 APIs, SNOMED CT and LOINC semantics, ISO 22600 access control, ABHA-linked OAuth 2.0 authentication, and robust audit trails for consent and versioning.\n\nTo operationalize this dual-coding approach, EMR vendors must implement a lightweight terminology micro-service that ingests NAMASTE CSV and synchronises with the WHO-11 ICD-API (Including Biomedicine and TM2). Within the EMR user interface, diagnosis entries should support auto-complete widgets that return both NAMASTE and ICD-11 (TM2 and Biomedicine) codes, comply with the Coding rules of ICD-11 framework and store them together in the patient’s Problem List resource. This integration empowers clinicians to combine traditional and biomedical insights, facilitates Ayush insurance claims under global ICD-11 coding rules, and provides the Ministry of Ayush with real-time morbidity analytics aligned with national and international reporting standards.\n\nDescription\n\nDesign and prototype an API integration that brings India’s NAMASTE terminologies, WHO Standardised International terminology and the WHO ICD-11 (Traditional Medicine Module 2 (TM2) & Biomedicine) into a FHIR-compliant Electronic Medical Record (EMR) system. Your goal is to enable clinicians to record traditional medicine diagnoses (Ayurveda, Siddha, Unani) using NAMASTE codes, then automatically map them to global ICD-11 (TM2 and Biomedicine) identifiers—supporting dual/double-coding (One code denoting Ayurveda/Siddha/Unani (TM) and another denoting Biomedicine) for interoperability, analytics and insurance claims.\n\nYour deliverable is a lightweight micro-service or FHIR terminology plugin offering:\n• A FHIR compliant resource for NAMASTE codes linking to WHO International Terminologies of Ayurveda, ICD-11 (TM2 and Biomedicine) and compliant with ICD-11 Coding rules.\n• A REST endpoint for auto-complete value-set lookup.\n• A translation operation converting NAMASTE ↔ TM2 codes.\n• An encounter upload endpoint that ingests FHIR Bundles with both code systems.\n• OAuth 2.0–secured access using ABHA tokens and audit-ready metadata.\n\nTeams should demonstrate\n\n1. Ingesting the NAMASTE CSV export and generating FHIR CodeSystem + ConceptMap.\n2. Fetching TM2, Biomedicine updates from the WHO ICD-API and merging into your service.\n3. A simple web or CLI interface to search NAMASTE terms, WHO International Terminologies of Ayurveda, see mapped TM2 codes, and construct a FHIR ProblemList entry.\n4. Version tracking and consent metadata to satisfy India’s 2016 EHR Standards (FHIR R4, ISO 22600, SNOMED-CT/LOINC semantics).\n\nExpected Solution\n\nA lightweight, FHIR R4–compliant terminology micro-service—built to India’s 2016 EHR Standards—that exposes a NAMASTE CodeSystem, WHO International Terminologies of Ayurveda, an ICD-11 TM2, Biomedicine ConceptMap, an auto-complete value-set lookup endpoint, a NAMASTE↔TM2 translate operation; ICD-11 Biomedicine look up and a secure FHIR Bundle upload interface (for enabling double coding).",
"ps_number": "SIH25026",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "MedTech / BioTech / HealthTech",
"title": "Develop API code to integrate NAMASTE and or the International Classification of Diseases (ICD-11) via the Traditional Medicine Module 2 (TM2) into existing EMR systems that comply with Electronic Health Record (EHR) Standards for India"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "The Ayurvedic herbal supply chain in India is characterised by fragmented networks of smallholder farmers, wild collectors and multiple intermediaries, leading to challenges in ensuring consistent quality, authenticity and sustainable sourcing of medicinal plants. Variations in harvesting practices, environmental conditions and manual record-keeping introduce risks of mislabeling, adulteration and over-harvesting of vulnerable species, undermining consumer trust and compliance with regulatory standards. Geographic provenance is often undocumented or opaque, making it difficult for manufacturers and regulators to verify that herbs originate from approved regions or follow sustainable collection guidelines.\n\nA blockchain-based traceability system, augmented with geo-tagging technology, can address these gaps by creating an immutable, decentralised ledger that records every step of the herb's journey—from on-site GPS-tagged collection events through processing, testing and formulation. Smart contracts on a permissioned network (e.g., Hyperledger Fabric) can enforce sustainability criteria and automate quality validations, while IoT-enabled devices capture real-time location and environmental data at remote collection points, even via SMS-over-blockchain gateways where connectivity is sparse. By integrating FHIR-style metadata bundles (e.g., \"CollectionEvent,\" \"QualityTest,\" \"ProcessingStep\") and QR-code scanning at aggregation nodes, stakeholders gain end-to-end visibility, enabling rapid verification of provenance, streamlined certification for export and robust audit trails to support both biodiversity conservation and supply-chain efficiency. When herbs are formulated into finished products, unique, serialised QR codes generated by the blockchain platform could be affixed to each package. End customers scan these codes with a mobile app or web portal—powered by the same blockchain ledger—to retrieve a FHIR-style provenance bundle detailing each upstream event: farm of origin, harvest conditions, intermediary custody, laboratory certificates and batch formulation parameters. This consumer-facing transparency not only verifies authenticity and builds trust, but also supports ethical marketing, enables rapid recall management and fosters incentives for sustainable collection practices by linking premium pricing to verified harvest data. Over time, analytics on consumer scans can feed back into demand forecasting, closing the loop between consumer assurance and supply-chain optimisation.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "A permissioned blockchain network will immutably record every stage of an Ayurvedic herb's journey—from geo-tagged harvest events by farmers or wild collectors, through multi-stage processing and laboratory testing, to the finished product on retail shelves. At the point of collection, GPS-enabled mobile or IoT devices capture precise location, timestamp, collector identity, species identification and initial quality metrics as a \"Collection Event.\" Subsequent \"Processing Step\" and \"Quality Test\" events—each embedding standardised metadata bundles—are added by processing facilities and testing laboratories. Smart contracts enforce National Medicinal Plants Board sustainability guidelines and Good Agricultural and Collection Practices by automatically validating geo-fencing rules, seasonal restrictions and quality thresholds before committing each transaction to the ledger.\n\nWhen formulation is complete, unique QR codes generated on-chain are affixed to product packaging. End customers scan these codes via a lightweight web or mobile portal (no specialised app required) to retrieve the full provenance record: farm coordinates and harvest conditions; chain-of-custody handoffs; lab certificates for moisture, pesticide and DNA-barcode tests; and sustainability and fair-trade compliance proofs. This consumer-facing transparency assures authenticity and safety, enables rapid recall notifications for affected batches and tells the story of each product—complete with interactive maps and farmer or community profiles. By combining tamper-proof audit trails, geo-tagged traceability and automated compliance enforcement, the system delivers a replicable model for ethical, sustainable and trust-driven Ayurvedic herb sourcing.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "Participants will deliver a proof-of-concept blockchain-based botanical traceability system addressing end-to-end provenance of Ayurvedic herbs. The solution should include the following core components and capabilities:\n\n1. Permissioned Blockchain Network\n• A lightweight, permissioned ledger (e.g., Hyperledger Fabric or Corda) that records every supply-chain transaction.\n• Network nodes representing farmers' cooperatives, wild-collector groups, testing laboratories, processing facilities and manufacturers.\n• Smart contracts enforcing:\n- Geo-fencing rules based on collectors' GPS coordinates and approved harvesting zones.\n- Seasonal-harvest restrictions and species-specific conservation limits per National Medicinal Plants Board guidelines.\n- Quality-gate validations (e.g., moisture thresholds, pesticide limits, DNA barcoding checks).\n\n2. Geo-Tagged Data Capture\n• IoT/GPS-enabled mobile DApp (or SMS-over-blockchain gateway) for collectors to record \"CollectionEvent\" metadata: latitude/longitude, timestamp, collector ID, species and initial quality metrics.\n• Sensor integrations or manual interfaces for \"QualityTest\" events (lab results) and \"ProcessingStep\" events (drying, grinding, storage conditions).\n\n3. Smart Labeling & Consumer Portal\n• On-chain generation of unique QR codes for each finished product batch.\n• A lightweight web/mobile portal (no specialised install required) allowing end customers to scan QR codes and retrieve a complete FHIR-style provenance bundle:\n- Collection location map and harvest details\n- Chain-of-custody handoffs through each supply-chain node\n- Laboratory certificates for moisture, pesticide analysis, DNA authentication\n- Sustainability compliance proofs and fair-trade verifications\n- Interactive farmer/community profiles and conservation credentials\n\n4. Integration & Interoperability\n• RESTful APIs for supply-chain managers to query real-time dashboards of harvest volumes, batch statuses, QA results and sustainability metrics.\n• Plugins or connectors to existing ERP/quality-management systems for seamless data exchange.\n• Use of FHIR-style resource models (CollectionEvent, QualityTest, ProcessingStep, Provenance) for standardized metadata exchange.\n\n5. User Interfaces & Reporting\n• A mobile DApp interface optimized for low-bandwidth rural environments, with offline data capture and SMS synchronization.\n• A web dashboard for stakeholders to monitor network health, query provenance records and generate compliance reports aligned with AYUSH Ministry export and sustainability requirements.\n• Automated reporting modules that compile environmental-impact metrics and conservation compliance data for certification bodies.\n\n6. Demonstration & Evaluation\n• A live pilot using one botanical species (e.g., Ashwagandha) across a small farming cooperative and a collaborating processor.\n• End-to-end demonstration: geo-tagging harvest, adding lab results, processing events, QR code scanning by simulated consumers and recall simulation.\n• Metrics collection on data-capture latency, transaction throughput, offline sync reliability and consumer-scan engagement.\n\nBy delivering these elements, participants will showcase a replicable, transparent and sustainable model for botanical traceability that bridges traditional Ayurvedic sourcing with modern blockchain technology and consumer empowerment.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Ayush",
"problem_description": "Background\n\nThe Ayurvedic herbal supply chain in India is characterised by fragmented networks of smallholder farmers, wild collectors and multiple intermediaries, leading to challenges in ensuring consistent quality, authenticity and sustainable sourcing of medicinal plants. Variations in harvesting practices, environmental conditions and manual record-keeping introduce risks of mislabeling, adulteration and over-harvesting of vulnerable species, undermining consumer trust and compliance with regulatory standards. Geographic provenance is often undocumented or opaque, making it difficult for manufacturers and regulators to verify that herbs originate from approved regions or follow sustainable collection guidelines.\n\nA blockchain-based traceability system, augmented with geo-tagging technology, can address these gaps by creating an immutable, decentralised ledger that records every step of the herb's journey—from on-site GPS-tagged collection events through processing, testing and formulation. Smart contracts on a permissioned network (e.g., Hyperledger Fabric) can enforce sustainability criteria and automate quality validations, while IoT-enabled devices capture real-time location and environmental data at remote collection points, even via SMS-over-blockchain gateways where connectivity is sparse. By integrating FHIR-style metadata bundles (e.g., \"CollectionEvent,\" \"QualityTest,\" \"ProcessingStep\") and QR-code scanning at aggregation nodes, stakeholders gain end-to-end visibility, enabling rapid verification of provenance, streamlined certification for export and robust audit trails to support both biodiversity conservation and supply-chain efficiency. When herbs are formulated into finished products, unique, serialised QR codes generated by the blockchain platform could be affixed to each package. End customers scan these codes with a mobile app or web portal—powered by the same blockchain ledger—to retrieve a FHIR-style provenance bundle detailing each upstream event: farm of origin, harvest conditions, intermediary custody, laboratory certificates and batch formulation parameters. This consumer-facing transparency not only verifies authenticity and builds trust, but also supports ethical marketing, enables rapid recall management and fosters incentives for sustainable collection practices by linking premium pricing to verified harvest data. Over time, analytics on consumer scans can feed back into demand forecasting, closing the loop between consumer assurance and supply-chain optimisation.\n\nDescription\n\nA permissioned blockchain network will immutably record every stage of an Ayurvedic herb's journey—from geo-tagged harvest events by farmers or wild collectors, through multi-stage processing and laboratory testing, to the finished product on retail shelves. At the point of collection, GPS-enabled mobile or IoT devices capture precise location, timestamp, collector identity, species identification and initial quality metrics as a \"Collection Event.\" Subsequent \"Processing Step\" and \"Quality Test\" events—each embedding standardised metadata bundles—are added by processing facilities and testing laboratories. Smart contracts enforce National Medicinal Plants Board sustainability guidelines and Good Agricultural and Collection Practices by automatically validating geo-fencing rules, seasonal restrictions and quality thresholds before committing each transaction to the ledger.\n\nWhen formulation is complete, unique QR codes generated on-chain are affixed to product packaging. End customers scan these codes via a lightweight web or mobile portal (no specialised app required) to retrieve the full provenance record: farm coordinates and harvest conditions; chain-of-custody handoffs; lab certificates for moisture, pesticide and DNA-barcode tests; and sustainability and fair-trade compliance proofs. This consumer-facing transparency assures authenticity and safety, enables rapid recall notifications for affected batches and tells the story of each product—complete with interactive maps and farmer or community profiles. By combining tamper-proof audit trails, geo-tagged traceability and automated compliance enforcement, the system delivers a replicable model for ethical, sustainable and trust-driven Ayurvedic herb sourcing.\n\nExpected Solution\n\nParticipants will deliver a proof-of-concept blockchain-based botanical traceability system addressing end-to-end provenance of Ayurvedic herbs. The solution should include the following core components and capabilities:\n\n1. Permissioned Blockchain Network\n• A lightweight, permissioned ledger (e.g., Hyperledger Fabric or Corda) that records every supply-chain transaction.\n• Network nodes representing farmers' cooperatives, wild-collector groups, testing laboratories, processing facilities and manufacturers.\n• Smart contracts enforcing:\n- Geo-fencing rules based on collectors' GPS coordinates and approved harvesting zones.\n- Seasonal-harvest restrictions and species-specific conservation limits per National Medicinal Plants Board guidelines.\n- Quality-gate validations (e.g., moisture thresholds, pesticide limits, DNA barcoding checks).\n\n2. Geo-Tagged Data Capture\n• IoT/GPS-enabled mobile DApp (or SMS-over-blockchain gateway) for collectors to record \"CollectionEvent\" metadata: latitude/longitude, timestamp, collector ID, species and initial quality metrics.\n• Sensor integrations or manual interfaces for \"QualityTest\" events (lab results) and \"ProcessingStep\" events (drying, grinding, storage conditions).\n\n3. Smart Labeling & Consumer Portal\n• On-chain generation of unique QR codes for each finished product batch.\n• A lightweight web/mobile portal (no specialised install required) allowing end customers to scan QR codes and retrieve a complete FHIR-style provenance bundle:\n- Collection location map and harvest details\n- Chain-of-custody handoffs through each supply-chain node\n- Laboratory certificates for moisture, pesticide analysis, DNA authentication\n- Sustainability compliance proofs and fair-trade verifications\n- Interactive farmer/community profiles and conservation credentials\n\n4. Integration & Interoperability\n• RESTful APIs for supply-chain managers to query real-time dashboards of harvest volumes, batch statuses, QA results and sustainability metrics.\n• Plugins or connectors to existing ERP/quality-management systems for seamless data exchange.\n• Use of FHIR-style resource models (CollectionEvent, QualityTest, ProcessingStep, Provenance) for standardized metadata exchange.\n\n5. User Interfaces & Reporting\n• A mobile DApp interface optimized for low-bandwidth rural environments, with offline data capture and SMS synchronization.\n• A web dashboard for stakeholders to monitor network health, query provenance records and generate compliance reports aligned with AYUSH Ministry export and sustainability requirements.\n• Automated reporting modules that compile environmental-impact metrics and conservation compliance data for certification bodies.\n\n6. Demonstration & Evaluation\n• A live pilot using one botanical species (e.g., Ashwagandha) across a small farming cooperative and a collaborating processor.\n• End-to-end demonstration: geo-tagging harvest, adding lab results, processing events, QR code scanning by simulated consumers and recall simulation.\n• Metrics collection on data-capture latency, transaction throughput, offline sync reliability and consumer-scan engagement.\n\nBy delivering these elements, participants will showcase a replicable, transparent and sustainable model for botanical traceability that bridges traditional Ayurvedic sourcing with modern blockchain technology and consumer empowerment.",
"ps_number": "SIH25027",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Blockchain & Cybersecurity",
"title": "Develop a blockchain-based system for botanical traceability of Ayurvedic herbs including geo-tagging from the point of collection (farmers/wild collectors) to the final Ayurvedic formulation label"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Higher Education institutions often face challenges in efficient class scheduling due to limited infrastructure, faculty constraints, elective courses, and overlapping departmental requirements. Manual timetable preparation leads to frequent clashes in classes, underutilized classrooms, uneven workload distribution, and dissatisfied students and faculty members. With the increasing adoption of multidisciplinary curricula and flexible learning under NEP 2020, the class scheduling process has become more complex and dynamic, requiring intelligent and adaptive solutions.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "The current scheduling mechanism in most higher education institutes/colleges relies on manual input via spreadsheets or basic tools. These fail to account for real-time availability of faculty, room capacity, teaching load norms, subject combinations, and student preferences. A solution is required that will accommodate the various parameters required for scheduling classes for UG and PG students and return an optimized timetable ensuring:\n• Maximized utilization of classrooms and laboratories\n• Minimized workload on faculty members and students\n• Achievement of required learning outcomes\n\nKey Parameters\n\nThe following parameters can be taken into account as variables for creating optimized timetables:\n- Number of classrooms available\n- Number of batches of students\n- Number of subjects to be taught in a particular semester\n- Names of subjects\n- Maximum number of classes per day\n- Number of classes to be conducted for a subject per week / per day\n- Number of faculties available for different subjects\n- Average number of leaves a faculty member takes in a month\n- Special classes that have fixed slots in timetable\n\nStudents may also consider additional variables that may help in effective timetable preparation.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "A web-based platform that can be linked to the college website. Authorized personnel will be able to login and input data against the listed variables to generate fully optimized timetables.\n\nThe platform should include:\n• Login facility for authorized personnel to create and manage timetables\n• Multiple options of optimized timetables to choose from\n• Review and approval workflow for competent authorities\n• Suggestions for suitable rearrangements when optimal solutions are not available\n• Support for multi-department and multi-shift scheduling",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Jharkhand",
"problem_description": "Background\n\nHigher Education institutions often face challenges in efficient class scheduling due to limited infrastructure, faculty constraints, elective courses, and overlapping departmental requirements. Manual timetable preparation leads to frequent clashes in classes, underutilized classrooms, uneven workload distribution, and dissatisfied students and faculty members. With the increasing adoption of multidisciplinary curricula and flexible learning under NEP 2020, the class scheduling process has become more complex and dynamic, requiring intelligent and adaptive solutions.\n\nDescription\n\nThe current scheduling mechanism in most higher education institutes/colleges relies on manual input via spreadsheets or basic tools. These fail to account for real-time availability of faculty, room capacity, teaching load norms, subject combinations, and student preferences. A solution is required that will accommodate the various parameters required for scheduling classes for UG and PG students and return an optimized timetable ensuring:\n• Maximized utilization of classrooms and laboratories\n• Minimized workload on faculty members and students\n• Achievement of required learning outcomes\n\nKey Parameters\n\nThe following parameters can be taken into account as variables for creating optimized timetables:\n- Number of classrooms available\n- Number of batches of students\n- Number of subjects to be taught in a particular semester\n- Names of subjects\n- Maximum number of classes per day\n- Number of classes to be conducted for a subject per week / per day\n- Number of faculties available for different subjects\n- Average number of leaves a faculty member takes in a month\n- Special classes that have fixed slots in timetable\n\nStudents may also consider additional variables that may help in effective timetable preparation.\n\nExpected Solution\n\nA web-based platform that can be linked to the college website. Authorized personnel will be able to login and input data against the listed variables to generate fully optimized timetables.\n\nThe platform should include:\n• Login facility for authorized personnel to create and manage timetables\n• Multiple options of optimized timetables to choose from\n• Review and approval workflow for competent authorities\n• Suggestions for suitable rearrangements when optimal solutions are not available\n• Support for multi-department and multi-shift scheduling",
"ps_number": "SIH25028",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Smart Classroom & Timetable Scheduler"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "With increasing digitization, the problem of fake degrees and forged academic certificates has become a major concern for higher education institutions, employers, and government bodies. Cases of fraudulent documents being used for jobs, admissions, or government schemes have highlighted the absence of a robust mechanism to verify the authenticity of educational credentials issued by colleges and universities.\n\nAt present, verification is often manual, relying on physical inspection, emails to institutions, or outdated databases. This creates delays, inconsistency, and susceptibility to corruption or manipulation. To preserve academic integrity and public trust, there is a pressing need for an efficient, secure, and scalable digital system to detect and prevent the use of fake degrees.\n\nDetailed Description\n\nThe challenge is to create a digital platform that can authenticate and detect fake degrees or certificates issued by higher education institutions across Jharkhand. The system should be able to cross-verify uploaded documents (PDFs, scans, etc.) with institutional databases or credential registries, using metadata, QR codes, signatures, or embedded hashes.\n\nSuch a platform must work with both legacy certificates (issued before digitization) and new ones generated under university ERP systems. It should detect anomalies such as:\n- Tampered grades or photos\n- Forged seals or signatures\n- Invalid certificate numbers\n- Non-existent institutions or courses\n- Duplicate or cloned documents\n\nIncorporating AI, OCR (Optical Character Recognition), and blockchain or cryptographic validation, the platform should enable seamless certificate verification by employers, admission offices, scholarship agencies, and government departments. The goal is to create a trustable and publicly accessible system that protects institutions’ reputation and safeguards student achievements.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "A smart, scalable, and secure Fake Degree/Certificate Recognition system that includes:\n• Upload interface for verifying entities (employers, institutions, agencies) to upload or input certificate details\n• Certificate authenticity checker that:\n – Uses OCR to extract key details (name, roll number, marks, certificate ID)\n – Matches it against a verified database (centralized or decentralized)\n – Flags mismatches or formatting inconsistencies\n• Digital watermark or blockchain verification support for newly issued certificates\n• Institution integration module for universities/colleges to upload their certificate records in bulk or in real-time\n• Admin dashboard for authorized bodies (e.g., Higher Education Department) to monitor verification activity, detect forgery trends, and blacklist offenders\n• Alert system for invalid or forged entries\n• Data privacy and access control measures to ensure secure handling of student information\n\nThis solution must be adaptable across different institutions, work with both physical and digital certificates, and be affordable for state-wide rollout.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Jharkhand",
"problem_description": "Background\n\nWith increasing digitization, the problem of fake degrees and forged academic certificates has become a major concern for higher education institutions, employers, and government bodies. Cases of fraudulent documents being used for jobs, admissions, or government schemes have highlighted the absence of a robust mechanism to verify the authenticity of educational credentials issued by colleges and universities.\n\nAt present, verification is often manual, relying on physical inspection, emails to institutions, or outdated databases. This creates delays, inconsistency, and susceptibility to corruption or manipulation. To preserve academic integrity and public trust, there is a pressing need for an efficient, secure, and scalable digital system to detect and prevent the use of fake degrees.\n\nDetailed Description\n\nThe challenge is to create a digital platform that can authenticate and detect fake degrees or certificates issued by higher education institutions across Jharkhand. The system should be able to cross-verify uploaded documents (PDFs, scans, etc.) with institutional databases or credential registries, using metadata, QR codes, signatures, or embedded hashes.\n\nSuch a platform must work with both legacy certificates (issued before digitization) and new ones generated under university ERP systems. It should detect anomalies such as:\n- Tampered grades or photos\n- Forged seals or signatures\n- Invalid certificate numbers\n- Non-existent institutions or courses\n- Duplicate or cloned documents\n\nIncorporating AI, OCR (Optical Character Recognition), and blockchain or cryptographic validation, the platform should enable seamless certificate verification by employers, admission offices, scholarship agencies, and government departments. The goal is to create a trustable and publicly accessible system that protects institutions’ reputation and safeguards student achievements.\n\nExpected Solution\n\nA smart, scalable, and secure Fake Degree/Certificate Recognition system that includes:\n• Upload interface for verifying entities (employers, institutions, agencies) to upload or input certificate details\n• Certificate authenticity checker that:\n – Uses OCR to extract key details (name, roll number, marks, certificate ID)\n – Matches it against a verified database (centralized or decentralized)\n – Flags mismatches or formatting inconsistencies\n• Digital watermark or blockchain verification support for newly issued certificates\n• Institution integration module for universities/colleges to upload their certificate records in bulk or in real-time\n• Admin dashboard for authorized bodies (e.g., Higher Education Department) to monitor verification activity, detect forgery trends, and blacklist offenders\n• Alert system for invalid or forged entries\n• Data privacy and access control measures to ensure secure handling of student information\n\nThis solution must be adaptable across different institutions, work with both physical and digital certificates, and be affordable for state-wide rollout.",
"ps_number": "SIH25029",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Authenticity Validator for Academia"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Farmers often face challenges in accessing timely, personalized, and accurate agricultural support. Language barriers, lack of technical knowledge, and limited reach of conventional advisory services reduce the effectiveness of existing solutions. Emerging generative AI technologies present an opportunity to deliver hyper-localized guidance in natural language, paired with visual understanding to assist with field-level problems like crop disease detection.\n\nDetailed Description\n\nThe objective is to create an AI-driven decision support system that determines real-time soil properties (pH, moisture, nutrient content) based on satellite data (e.g., Soil Grids, Bhuvan APIs) or IoT sensors. The system must also account for localized weather forecasts, past crop rotation data to preserve soil fertility, and existing market demand and price trends obtained through APIs or scraping agri-market websites.\n\nA machine learning model will provide the most appropriate crops for specified conditions, forecasting yield, profit margins, and sustainability scores. The solution should comprise a mobile application with a simple multilingual interface where farmers can enter or retrieve relevant data, see recommendations, and operate offline in low-connectivity regions.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "A mobile-based prototype offering farmers customized, science-guided crop advice, increasing income, making resources more efficient, and facilitating sustainable agriculture.\nThe solution should deliver an AI-powered platform that integrates text and image-based interactions tailored for agricultural use. It must support voice and chat interfaces in local languages, enabling farmers to ask questions and receive actionable responses.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Jharkhand",
"problem_description": "Background\n\nFarmers often face challenges in accessing timely, personalized, and accurate agricultural support. Language barriers, lack of technical knowledge, and limited reach of conventional advisory services reduce the effectiveness of existing solutions. Emerging generative AI technologies present an opportunity to deliver hyper-localized guidance in natural language, paired with visual understanding to assist with field-level problems like crop disease detection.\n\nDetailed Description\n\nThe objective is to create an AI-driven decision support system that determines real-time soil properties (pH, moisture, nutrient content) based on satellite data (e.g., Soil Grids, Bhuvan APIs) or IoT sensors. The system must also account for localized weather forecasts, past crop rotation data to preserve soil fertility, and existing market demand and price trends obtained through APIs or scraping agri-market websites.\n\nA machine learning model will provide the most appropriate crops for specified conditions, forecasting yield, profit margins, and sustainability scores. The solution should comprise a mobile application with a simple multilingual interface where farmers can enter or retrieve relevant data, see recommendations, and operate offline in low-connectivity regions.\n\nExpected Solution\n\nA mobile-based prototype offering farmers customized, science-guided crop advice, increasing income, making resources more efficient, and facilitating sustainable agriculture.\nThe solution should deliver an AI-powered platform that integrates text and image-based interactions tailored for agricultural use. It must support voice and chat interfaces in local languages, enabling farmers to ask questions and receive actionable responses.",
"ps_number": "SIH25030",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "AI-Based Crop Recommendation for Farmers"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Local governments often face challenges in promptly identifying, prioritizing, and resolving everyday civic issues like potholes, malfunctioning streetlights, or overflowing trash bins. While citizens may encounter these issues daily, a lack of effective reporting and tracking mechanisms limits municipal responsiveness. A streamlined, mobile-first solution can bridge this gap by empowering community members to submit real-world reports that municipalities can systematically address.\n\nDetailed Description\n\nThe system revolves around an easy-to-use mobile interface that allows users to submit reports in real-time. Each report can contain a photo, automatic location tagging, and a short text or voice explanation, providing sufficient context. These submissions populate a centralized dashboard featuring a live, interactive map of the city's reported issues. The system highlights priority areas based on volume of submissions, urgency inferred from user inputs, or other configurable criteria.\n\nOn the administrative side, staff access a powerful dashboard where they can view, filter, and categorize incoming reports. Automated routing directs each report to the relevant department such as sanitation or public works based on the issue type and location. System architecture accommodates spikes in reporting, ensuring quick image uploads, responsive performance across devices, and near real-time updates on both mobile and desktop clients.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "The final deliverable should include a mobile platform that supports cross-device functionality and seamless user experience. Citizens must be able to capture issues effortlessly, track the progress of their reports, and receive notifications through each stage — confirmation, acknowledgment, and resolution.\nOn the back end, a web-based administrative portal should enable municipal staff to filter issues by category, location, or priority, assign tasks, update statuses, and communicate progress. The platform should integrate an automated routing engine that leverages report metadata to correctly allocate tasks to departments.\nA scalable, resilient backend must manage high volumes of multimedia content, support concurrent users, and provide APIs for future integrations or extensions. Lastly, the solution should deliver analytics and reporting features that offer insights into reporting trends, departmental response times, and overall system effectiveness — ultimately driving better civic engagement and government accountability.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Jharkhand",
"problem_description": "Background\n\nLocal governments often face challenges in promptly identifying, prioritizing, and resolving everyday civic issues like potholes, malfunctioning streetlights, or overflowing trash bins. While citizens may encounter these issues daily, a lack of effective reporting and tracking mechanisms limits municipal responsiveness. A streamlined, mobile-first solution can bridge this gap by empowering community members to submit real-world reports that municipalities can systematically address.\n\nDetailed Description\n\nThe system revolves around an easy-to-use mobile interface that allows users to submit reports in real-time. Each report can contain a photo, automatic location tagging, and a short text or voice explanation, providing sufficient context. These submissions populate a centralized dashboard featuring a live, interactive map of the city's reported issues. The system highlights priority areas based on volume of submissions, urgency inferred from user inputs, or other configurable criteria.\n\nOn the administrative side, staff access a powerful dashboard where they can view, filter, and categorize incoming reports. Automated routing directs each report to the relevant department such as sanitation or public works based on the issue type and location. System architecture accommodates spikes in reporting, ensuring quick image uploads, responsive performance across devices, and near real-time updates on both mobile and desktop clients.\n\nExpected Solution\n\nThe final deliverable should include a mobile platform that supports cross-device functionality and seamless user experience. Citizens must be able to capture issues effortlessly, track the progress of their reports, and receive notifications through each stage — confirmation, acknowledgment, and resolution.\nOn the back end, a web-based administrative portal should enable municipal staff to filter issues by category, location, or priority, assign tasks, update statuses, and communicate progress. The platform should integrate an automated routing engine that leverages report metadata to correctly allocate tasks to departments.\nA scalable, resilient backend must manage high volumes of multimedia content, support concurrent users, and provide APIs for future integrations or extensions. Lastly, the solution should deliver analytics and reporting features that offer insights into reporting trends, departmental response times, and overall system effectiveness — ultimately driving better civic engagement and government accountability.",
"ps_number": "SIH25031",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Clean & Green Technology",
"title": "Crowdsourced Civic Issue Reporting and Resolution System"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Jharkhand is blessed with natural beauty, tribal culture, historical landmarks, and eco-tourism hotspots like Netarhat, Patratu, Betla National Park, Hundru Falls, and Deoghar. However, despite its vast potential, the state's tourism industry remains underdeveloped due to a lack of digital infrastructure, limited promotional outreach, low tourist awareness, and unorganized travel and hospitality services.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Tourists often find it difficult to access reliable information about destinations, local transportation, accommodations, cultural activities, and guides. Additionally, local artisans, tribal communities, and small-scale service providers who could benefit greatly from tourism remain largely excluded from the digital ecosystem. There is a strong need for a centralized digital platform that not only improves the tourist experience through authentic and accessible information but also connects and empowers local communities, making tourism in Jharkhand more inclusive, organized, and sustainable.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "Design and develop an AI-powered digital tourism platform (mobile app and/or website) for Jharkhand that offers:\n• AI-based personalized itinerary planning and multilingual chatbot assistance for tourists.\n• Blockchain-enabled secure transactions, guide verification, and digital certification for local service providers.\n• Interactive maps and AR/VR previews of major tourist and cultural sites.\n• Real-time transport and location info using geo-location.\n• Integrated local marketplace for tribal handicrafts, events, homestays, and ecotourism.\n• AI-driven feedback and sentiment analysis for continuous improvement.\n• Analytics dashboard for tourism officials to monitor trends and impact.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Jharkhand",
"problem_description": "Background\n\nJharkhand is blessed with natural beauty, tribal culture, historical landmarks, and eco-tourism hotspots like Netarhat, Patratu, Betla National Park, Hundru Falls, and Deoghar. However, despite its vast potential, the state's tourism industry remains underdeveloped due to a lack of digital infrastructure, limited promotional outreach, low tourist awareness, and unorganized travel and hospitality services.\n\nDescription\n\nTourists often find it difficult to access reliable information about destinations, local transportation, accommodations, cultural activities, and guides. Additionally, local artisans, tribal communities, and small-scale service providers who could benefit greatly from tourism remain largely excluded from the digital ecosystem. There is a strong need for a centralized digital platform that not only improves the tourist experience through authentic and accessible information but also connects and empowers local communities, making tourism in Jharkhand more inclusive, organized, and sustainable.\n\nExpected Solution\n\nDesign and develop an AI-powered digital tourism platform (mobile app and/or website) for Jharkhand that offers:\n• AI-based personalized itinerary planning and multilingual chatbot assistance for tourists.\n• Blockchain-enabled secure transactions, guide verification, and digital certification for local service providers.\n• Interactive maps and AR/VR previews of major tourist and cultural sites.\n• Real-time transport and location info using geo-location.\n• Integrated local marketplace for tribal handicrafts, events, homestays, and ecotourism.\n• AI-driven feedback and sentiment analysis for continuous improvement.\n• Analytics dashboard for tourism officials to monitor trends and impact.",
"ps_number": "SIH25032",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Travel & Tourism",
"title": "Development of a Smart Digital Platform to Promote Eco & Cultural Tourism in Jharkhand"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "The PM Internship Scheme enables students to gain industry exposure through structured internships. However, matching thousands of applicants with the most suitable opportunities remains a challenge, often leading to suboptimal selections and delays.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "The problem envisages a smart, automated system that uses AI/ML algorithms to match candidates with internship opportunities based on skills, qualifications, location preferences, and sector interests. The system should also account for affirmative action (e.g., representation from rural/aspirational districts, different social categories), past participation, and internship capacity of industries.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "A functional prototype with:\n• AI-based matchmaking engine for internship placement\n• A prototype of the front end demonstrating how this engine will work",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Corporate Affairs",
"problem_description": "Background\n\nThe PM Internship Scheme enables students to gain industry exposure through structured internships. However, matching thousands of applicants with the most suitable opportunities remains a challenge, often leading to suboptimal selections and delays.\n\nDescription\n\nThe problem envisages a smart, automated system that uses AI/ML algorithms to match candidates with internship opportunities based on skills, qualifications, location preferences, and sector interests. The system should also account for affirmative action (e.g., representation from rural/aspirational districts, different social categories), past participation, and internship capacity of industries.\n\nExpected Solution\n\nA functional prototype with:\n• AI-based matchmaking engine for internship placement\n• A prototype of the front end demonstrating how this engine will work",
"ps_number": "SIH25033",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "AI-Based Smart Allocation Engine for PM Internship Scheme"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "The PM Internship Scheme receives applications from youth across India, including rural areas, tribal districts, urban slums, and remote colleges. Many of these candidates are first-generation learners with limited digital exposure and no prior internship experience. With hundreds of internships listed on the portal, it becomes difficult for such candidates to identify which ones match their skills, interests, or aspirations. This leads to misaligned applications and missed opportunities.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "The problem seeks to build a simple, lightweight AI-based recommendation engine that suggests the most relevant internships to each candidate based on their profile, academic background, interests, and location preferences. The system should be user-friendly, mobile-compatible, and work well even for users with low digital literacy. It should offer 3-5 personalized suggestions, instead of a long list, and help candidates make informed choices. The tool must be simple enough to be integrated with the existing PM Internship Scheme portal and must avoid complex or resource-intensive deployment.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "A functional prototype that:\n• Captures basic candidate inputs (education, skills, sector interests, location)\n• Uses a rule-based or ML-light model to suggest 3-5 top internships\n• Has a simple, intuitive UI with minimal text and visual cues\n• Can be accessed on mobile devices and adapted to regional language use\n• Outputs recommendations in a clear, user-friendly format (e.g., cards or simple list)",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Corporate Affairs",
"problem_description": "Background\n\nThe PM Internship Scheme receives applications from youth across India, including rural areas, tribal districts, urban slums, and remote colleges. Many of these candidates are first-generation learners with limited digital exposure and no prior internship experience. With hundreds of internships listed on the portal, it becomes difficult for such candidates to identify which ones match their skills, interests, or aspirations. This leads to misaligned applications and missed opportunities.\n\nDescription\n\nThe problem seeks to build a simple, lightweight AI-based recommendation engine that suggests the most relevant internships to each candidate based on their profile, academic background, interests, and location preferences. The system should be user-friendly, mobile-compatible, and work well even for users with low digital literacy. It should offer 3-5 personalized suggestions, instead of a long list, and help candidates make informed choices. The tool must be simple enough to be integrated with the existing PM Internship Scheme portal and must avoid complex or resource-intensive deployment.\n\nExpected Solution\n\nA functional prototype that:\n• Captures basic candidate inputs (education, skills, sector interests, location)\n• Uses a rule-based or ML-light model to suggest 3-5 top internships\n• Has a simple, intuitive UI with minimal text and visual cues\n• Can be accessed on mobile devices and adapted to regional language use\n• Outputs recommendations in a clear, user-friendly format (e.g., cards or simple list)",
"ps_number": "SIH25034",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "AI-Based Internship Recommendation Engine for PM Internship Scheme"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "eConsultation module is an online platform wherein proposed amendments/draft legislations are posted on MCA's website for external users to submit their comments and suggestions pertaining to the same through the MCA21 portal. The comments are captured in a structured format for due consideration with respect to amending the draft legislation, based on the suggestions or observations received.\n\nProblem Statement\n\nThe draft document soliciting comments is made available for a specified period, during which any stakeholder may submit their observations either on the overall amendment or on specific provisions of the draft legislation. In instances where a substantial volume of comments is received on draft legislation, there exists a risk of certain observations being inadvertently overlooked or inadequately analysed. In order to review each individual submission, leveraging AI-assisted tools will help ensure that all remarks are duly considered and systematically analysed. Requirement is the development of an AI model aimed at predicting the sentiments of the suggestions provided by stakeholders in the eConsultation module. It should also generate a visual representation in the form of a word cloud, highlighting the keywords utilised by the stakeholders within their suggestions.\n\nExpected Outcome\n\nThe intention is to discern the feedback received from the stakeholders through the following:\n• Sentiment analysis\n• Summary generation\n• Word cloud\n\nThe solution should considerably reduce the effort of the end user in analysing a high volume of comments. It should be able to clearly identify the sentiments of comments individually as well as broadly overall. The summary generation should be accurate and convey the meaning of the comment properly, in a precise manner. The word cloud feature should showcase the density of the words used by all users.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Corporate Affairs",
"problem_description": "Background\n\neConsultation module is an online platform wherein proposed amendments/draft legislations are posted on MCA's website for external users to submit their comments and suggestions pertaining to the same through the MCA21 portal. The comments are captured in a structured format for due consideration with respect to amending the draft legislation, based on the suggestions or observations received.\n\nProblem Statement\n\nThe draft document soliciting comments is made available for a specified period, during which any stakeholder may submit their observations either on the overall amendment or on specific provisions of the draft legislation. In instances where a substantial volume of comments is received on draft legislation, there exists a risk of certain observations being inadvertently overlooked or inadequately analysed. In order to review each individual submission, leveraging AI-assisted tools will help ensure that all remarks are duly considered and systematically analysed. Requirement is the development of an AI model aimed at predicting the sentiments of the suggestions provided by stakeholders in the eConsultation module. It should also generate a visual representation in the form of a word cloud, highlighting the keywords utilised by the stakeholders within their suggestions.\n\nExpected Outcome\n\nThe intention is to discern the feedback received from the stakeholders through the following:\n• Sentiment analysis\n• Summary generation\n• Word cloud\n\nThe solution should considerably reduce the effort of the end user in analysing a high volume of comments. It should be able to clearly identify the sentiments of comments individually as well as broadly overall. The summary generation should be accurate and convey the meaning of the comment properly, in a precise manner. The word cloud feature should showcase the density of the words used by all users.",
"ps_number": "SIH25035",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Sentiment analysis of comments received through E-consultation module"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "Microplastics — plastic particles less than 5 mm in size — have emerged as a global environmental threat, contaminating oceans, freshwater systems, and even drinking water. These particles originate from the degradation of larger plastics or from microbeads in consumer products. Their persistence and bioaccumulative nature pose serious risks to aquatic life, ecosystems, and potentially human health. Current methods for detecting microplastics — such as Fourier-transform infrared spectroscopy (FTIR) and Raman spectroscopy — are often accurate but time-consuming, expensive, and laboratory-dependent, limiting their application for real-time, field-based monitoring.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "The project aims to develop a low-cost, efficient, and portable sensor capable of detecting microplastics in various water sources. The focus may be on using optical detection techniques — such as light scattering, fluorescence, or absorbance — combined with machine learning algorithms for pattern recognition. The sensor may be designed to identify microplastic particles based on their optical signatures, size distribution, and possibly polymer type. Water samples shall pass through a detection chamber, where embedded sensors capture spectral or visual data. This information would be processed in real time, either on-device or via a connected application. The system should be tested using standard microplastic samples (e.g., polyethylene, polypropylene) and validated against existing lab-based techniques.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "The expected outcome is a compact, field-deployable sensor system that can detect and quantify microplastics in water samples within minutes. It should offer reasonable sensitivity (down to 10–100 μm), operate on battery or solar power, and require minimal sample preparation. Additionally, the solution may include a companion software interface for data logging, visualization, and analysis. This innovation should have strong potential for adoption in microplastic monitoring, supporting researchers, environmental agencies, and water quality managers in making faster, data-driven decisions for pollution control and mitigation.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Earth Sciences (MoES)",
"problem_description": "Background\n\nMicroplastics — plastic particles less than 5 mm in size — have emerged as a global environmental threat, contaminating oceans, freshwater systems, and even drinking water. These particles originate from the degradation of larger plastics or from microbeads in consumer products. Their persistence and bioaccumulative nature pose serious risks to aquatic life, ecosystems, and potentially human health. Current methods for detecting microplastics — such as Fourier-transform infrared spectroscopy (FTIR) and Raman spectroscopy — are often accurate but time-consuming, expensive, and laboratory-dependent, limiting their application for real-time, field-based monitoring.\n\nDescription\n\nThe project aims to develop a low-cost, efficient, and portable sensor capable of detecting microplastics in various water sources. The focus may be on using optical detection techniques — such as light scattering, fluorescence, or absorbance — combined with machine learning algorithms for pattern recognition. The sensor may be designed to identify microplastic particles based on their optical signatures, size distribution, and possibly polymer type. Water samples shall pass through a detection chamber, where embedded sensors capture spectral or visual data. This information would be processed in real time, either on-device or via a connected application. The system should be tested using standard microplastic samples (e.g., polyethylene, polypropylene) and validated against existing lab-based techniques.\n\nExpected Solution\n\nThe expected outcome is a compact, field-deployable sensor system that can detect and quantify microplastics in water samples within minutes. It should offer reasonable sensitivity (down to 10–100 μm), operate on battery or solar power, and require minimal sample preparation. Additionally, the solution may include a companion software interface for data logging, visualization, and analysis. This innovation should have strong potential for adoption in microplastic monitoring, supporting researchers, environmental agencies, and water quality managers in making faster, data-driven decisions for pollution control and mitigation.",
"ps_number": "SIH25036",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Development of Sensor for Detection Of Microplastics"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "Grain size is a fundamental property used to classify beach types and plays a critical role in influencing coastal morphodynamic processes. However, measuring grain size is particularly challenging, as it requires scientists to visit each site to collect sediment samples. These samples must then undergo time-consuming physical processing in the laboratory to determine their characteristics. Moreover, because beach sediments are dynamic and constantly changing due to wave action, tides, and weather events, this process must be repeated regularly to maintain accurate and up-to-date data.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "The above problem statement envisages that a low-cost camera-based automated mapping system be developed to estimate the sediment grain size distribution of the sandy beach area (i.e., berm, intertidal, and dune) and classify the beach category of the measurement region. A low-cost camera-based mapping system, along with automated prediction algorithms, is used to estimate the sediment grain size distribution of a particular area of interest to be developed. Also, this system should incorporate a GNSS/GPS receiver to map the geographic positions of the measurement location.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "A low-cost camera-based mapping system with a GNSS/GPS receiver should be developed with an automated image processing algorithm for beach sand grain size estimation/classification. Validation should be performed at least on one sample based on data collected in the area of interest.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Earth Sciences (MoES)",
"problem_description": "Background\n\nGrain size is a fundamental property used to classify beach types and plays a critical role in influencing coastal morphodynamic processes. However, measuring grain size is particularly challenging, as it requires scientists to visit each site to collect sediment samples. These samples must then undergo time-consuming physical processing in the laboratory to determine their characteristics. Moreover, because beach sediments are dynamic and constantly changing due to wave action, tides, and weather events, this process must be repeated regularly to maintain accurate and up-to-date data.\n\nDescription\n\nThe above problem statement envisages that a low-cost camera-based automated mapping system be developed to estimate the sediment grain size distribution of the sandy beach area (i.e., berm, intertidal, and dune) and classify the beach category of the measurement region. A low-cost camera-based mapping system, along with automated prediction algorithms, is used to estimate the sediment grain size distribution of a particular area of interest to be developed. Also, this system should incorporate a GNSS/GPS receiver to map the geographic positions of the measurement location.\n\nExpected Solution\n\nA low-cost camera-based mapping system with a GNSS/GPS receiver should be developed with an automated image processing algorithm for beach sand grain size estimation/classification. Validation should be performed at least on one sample based on data collected in the area of interest.",
"ps_number": "SIH25037",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Development of a low-cost camera-based automated beach sand grain size mapping system"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Blue carbon ecosystem restoration is gaining importance in India’s climate strategy. However, there is no decentralized, verifiable Monitoring, Reporting, and Verification (MRV) system that ensures transparency, accuracy, and carbon credit generation.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Participants are to design a Blockchain-powered registry where:\n• Verified plantation and restoration data are immutably stored.\n• Carbon credits are tokenized using smart contracts.\n• NGOs, communities, and coastal panchayats can be onboarded.\n• Field data is integrated from apps and drones.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "• Blockchain app for blue carbon MRV.\n• Smart contracts for tokenized credits.\n• Mobile interface for data uploads.\n• Admin tools for NCCR.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Earth Sciences (MoES)",
"problem_description": "Background\n\nBlue carbon ecosystem restoration is gaining importance in India’s climate strategy. However, there is no decentralized, verifiable Monitoring, Reporting, and Verification (MRV) system that ensures transparency, accuracy, and carbon credit generation.\n\nDescription\n\nParticipants are to design a Blockchain-powered registry where:\n• Verified plantation and restoration data are immutably stored.\n• Carbon credits are tokenized using smart contracts.\n• NGOs, communities, and coastal panchayats can be onboarded.\n• Field data is integrated from apps and drones.\n\nExpected Solution\n\n• Blockchain app for blue carbon MRV.\n• Smart contracts for tokenized credits.\n• Mobile interface for data uploads.\n• Admin tools for NCCR.",
"ps_number": "SIH25038",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Clean & Green Technology",
"title": "Blockchain-Based Blue Carbon Registry and MRV System"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "India’s vast coastline is vulnerable to a range of ocean hazards such as tsunamis, storm surges, high waves, coastal currents, and abnormal sea behaviour. While agencies like INCOIS provide early warnings based on satellite data, sensors, and numerical models; real-time field reporting from citizens and local communities are often unavailable or delayed. Additionally, valuable insights from public discussions on social media during hazard events remain untapped, yet can be critical for understanding ground realities, public awareness, and the spread of information.\n\nDetailed Description\n\nThere is a need for a unified platform that enables citizens, coastal residents, volunteers, and disaster managers to report observations during hazardous ocean events (e.g., unusual tides, flooding, coastal damage, tsunami, swell surges, high waves, etc.) and to monitor public communication trends via social media.\n\nThis platform should:\n• Allow citizens to submit geotagged reports, photos, or videos of observed ocean hazards via a mobile/web app.\n• Support role-based access for citizens, officials, and analysts.\n• Aggregate and visualize real-time crowdsourced data on a dynamic dashboard.\n• Visualize all crowdsourced reports and social media indicators on an interactive map, with hotspots dynamically generated based on report density, keyword frequency, or verified incidents.\n• Integrate social media feeds (e.g., Twitter, public Facebook posts, YouTube comments) and apply Text Classification/Natural Language Processing to extract hazard-related discussions and trends.\n• Help emergency response agencies understand the scale, urgency, and sentiment of hazard events.\n• Provide filters by location, event type, date, and source, enabling better situational awareness and faster validation of warning models.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "An integrated software platform (mobile + web) with:\n• User registration and reporting interface with media upload.\n• Map-based dashboard showing live crowd reports and social media activity.\n• Dynamic hotspot generation based on report volume or verified threat indicators.\n• Backend database and API for data management and integration with early warning systems.\n• NLP engine for detecting relevant hazard-related posts, keywords, and engagement metrics.\n• Multilingual support for regional accessibility.\n• Offline data collection capabilities (sync later), useful for remote coastal areas.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "About INCOIS\n\nThe Indian National Centre for Ocean Information Services (INCOIS), operating under the administrative control of the Ministry of Earth Sciences, provides ocean information and advisory services to support disaster risk reduction and ensure maritime safety for coastal stakeholders. Its early warning services cover hazards such as tsunamis, storm surges, high waves, swell surges, and coastal currents, enabling authorities and communities to make informed decisions during ocean-related emergencies.",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Earth Sciences (MoES)",
"problem_description": "About INCOIS\n\nThe Indian National Centre for Ocean Information Services (INCOIS), operating under the administrative control of the Ministry of Earth Sciences, provides ocean information and advisory services to support disaster risk reduction and ensure maritime safety for coastal stakeholders. Its early warning services cover hazards such as tsunamis, storm surges, high waves, swell surges, and coastal currents, enabling authorities and communities to make informed decisions during ocean-related emergencies.\n\nBackground\n\nIndia’s vast coastline is vulnerable to a range of ocean hazards such as tsunamis, storm surges, high waves, coastal currents, and abnormal sea behaviour. While agencies like INCOIS provide early warnings based on satellite data, sensors, and numerical models; real-time field reporting from citizens and local communities are often unavailable or delayed. Additionally, valuable insights from public discussions on social media during hazard events remain untapped, yet can be critical for understanding ground realities, public awareness, and the spread of information.\n\nDetailed Description\n\nThere is a need for a unified platform that enables citizens, coastal residents, volunteers, and disaster managers to report observations during hazardous ocean events (e.g., unusual tides, flooding, coastal damage, tsunami, swell surges, high waves, etc.) and to monitor public communication trends via social media.\n\nThis platform should:\n• Allow citizens to submit geotagged reports, photos, or videos of observed ocean hazards via a mobile/web app.\n• Support role-based access for citizens, officials, and analysts.\n• Aggregate and visualize real-time crowdsourced data on a dynamic dashboard.\n• Visualize all crowdsourced reports and social media indicators on an interactive map, with hotspots dynamically generated based on report density, keyword frequency, or verified incidents.\n• Integrate social media feeds (e.g., Twitter, public Facebook posts, YouTube comments) and apply Text Classification/Natural Language Processing to extract hazard-related discussions and trends.\n• Help emergency response agencies understand the scale, urgency, and sentiment of hazard events.\n• Provide filters by location, event type, date, and source, enabling better situational awareness and faster validation of warning models.\n\nExpected Solution\n\nAn integrated software platform (mobile + web) with:\n• User registration and reporting interface with media upload.\n• Map-based dashboard showing live crowd reports and social media activity.\n• Dynamic hotspot generation based on report volume or verified threat indicators.\n• Backend database and API for data management and integration with early warning systems.\n• NLP engine for detecting relevant hazard-related posts, keywords, and engagement metrics.\n• Multilingual support for regional accessibility.\n• Offline data collection capabilities (sync later), useful for remote coastal areas.",
"ps_number": "SIH25039",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Disaster Management",
"title": "Integrated Platform for Crowdsourced Ocean Hazard Reporting and Social Media Analytics"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Oceanographic data is vast, complex, and heterogeneous – ranging from satellite observations to in-situ measurements like CTD casts, Argo floats, and BGC sensors. The Argo program, which deploys autonomous profiling floats across the world’s oceans, generates an extensive dataset in NetCDF format containing temperature, salinity, and other essential ocean variables. Accessing, querying, and visualizing this data requires domain knowledge, technical skills, and familiarity with complex formats and tools. With the rise of AI and Large Language Models (LLMs), especially when combined with modern structured databases and interactive dashboards, it is now feasible to create intuitive, accessible systems that democratize access to ocean data.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "The current problem statement proposes the development of an AI-powered conversational system for ARGO float data that enables users to query, explore, and visualize oceanographic information using natural language.\n\nThe current system shall:\n− Ingest ARGO NetCDF files and convert them into structured formats (like SQL/Parquet).\n− Use a vector database (like FAISS/Chroma) to store metadata and summaries for retrieval.\n− Leverage Retrieval-Augmented Generation (RAG) pipelines powered by multimodal LLMs (such as GPT, QWEN, LLaMA, or Mistral) to interpret user queries and map them to database queries (SQL). (Use Model Context Protocol (MCP))\n− Enable interactive dashboards (via Streamlit or Dash) for visualization of ARGO profiles, such as mapped trajectories, depth-time plots, and profile comparisons, etc.\n− Provide a chatbot-style interface where users can ask questions like:\n • Show me salinity profiles near the equator in March 2023\n • Compare BGC parameters in the Arabian Sea for the last 6 months\n • What are the nearest ARGO floats to this location?\n\nThis tool will bridge the gap between domain experts, decision-makers, and raw data by allowing non-technical users to extract meaningful insights effortlessly.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "− End-to-end pipeline to process ARGO NetCDF data and store it in a relational (PostgreSQL) and vector database (FAISS/Chroma).\n− Backend LLM system that translates natural language into database queries and generates responses using RAG.\n− Frontend dashboard with geospatial visualizations (using Plotly, Leaflet, or Cesium) and tabular summaries to ASCII, NetCDF.\n− Chat interface that understands user intent and guides them through data discovery.\n− Demonstrate a working Proof-of-Concept (PoC) with Indian Ocean ARGO data and future extensibility to in-situ observations (BGC, glider, buoys, etc.), and satellite datasets.\n\nAcronyms\n\nNetCDF: Network Common Data Format\nCTD: Conductivity Temperature and Depth\nBGC: Bio-Geo-Chemical Floats",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Earth Sciences (MoES)",
"problem_description": "Background\n\nOceanographic data is vast, complex, and heterogeneous – ranging from satellite observations to in-situ measurements like CTD casts, Argo floats, and BGC sensors. The Argo program, which deploys autonomous profiling floats across the world’s oceans, generates an extensive dataset in NetCDF format containing temperature, salinity, and other essential ocean variables. Accessing, querying, and visualizing this data requires domain knowledge, technical skills, and familiarity with complex formats and tools. With the rise of AI and Large Language Models (LLMs), especially when combined with modern structured databases and interactive dashboards, it is now feasible to create intuitive, accessible systems that democratize access to ocean data.\n\nDescription\n\nThe current problem statement proposes the development of an AI-powered conversational system for ARGO float data that enables users to query, explore, and visualize oceanographic information using natural language.\n\nThe current system shall:\n− Ingest ARGO NetCDF files and convert them into structured formats (like SQL/Parquet).\n− Use a vector database (like FAISS/Chroma) to store metadata and summaries for retrieval.\n− Leverage Retrieval-Augmented Generation (RAG) pipelines powered by multimodal LLMs (such as GPT, QWEN, LLaMA, or Mistral) to interpret user queries and map them to database queries (SQL). (Use Model Context Protocol (MCP))\n− Enable interactive dashboards (via Streamlit or Dash) for visualization of ARGO profiles, such as mapped trajectories, depth-time plots, and profile comparisons, etc.\n− Provide a chatbot-style interface where users can ask questions like:\n • Show me salinity profiles near the equator in March 2023\n • Compare BGC parameters in the Arabian Sea for the last 6 months\n • What are the nearest ARGO floats to this location?\n\nThis tool will bridge the gap between domain experts, decision-makers, and raw data by allowing non-technical users to extract meaningful insights effortlessly.\n\nExpected Solution\n\n− End-to-end pipeline to process ARGO NetCDF data and store it in a relational (PostgreSQL) and vector database (FAISS/Chroma).\n− Backend LLM system that translates natural language into database queries and generates responses using RAG.\n− Frontend dashboard with geospatial visualizations (using Plotly, Leaflet, or Cesium) and tabular summaries to ASCII, NetCDF.\n− Chat interface that understands user intent and guides them through data discovery.\n− Demonstrate a working Proof-of-Concept (PoC) with Indian Ocean ARGO data and future extensibility to in-situ observations (BGC, glider, buoys, etc.), and satellite datasets.\n\nAcronyms\n\nNetCDF: Network Common Data Format\nCTD: Conductivity Temperature and Depth\nBGC: Bio-Geo-Chemical Floats",
"ps_number": "SIH25040",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "FloatChat - AI-Powered Conversational Interface for ARGO Ocean Data Discovery and Visualization"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "India, with its vast coastline and rich marine biodiversity, relies heavily on the health of its ocean ecosystems for food security, climate regulation, and economic development. The Centre for Marine Living Resources and Ecology (CMLRE), under the Ministry of Earth Sciences, plays a pivotal role in collecting, managing, and analysing a broad spectrum of marine data to support sustainable ocean resource management.\n\nDetailed description\n\nCMLRE's datasets are diverse, complex, and multidisciplinary, including physical, chemical, and biological oceanography; fish abundance, species diversity, life history traits, and ecomorphology; fish taxonomy and otolith morphology; molecular biology data, including environmental DNA (eDNA) for species detection and biodiversity assessments. These datasets exist across various formats (structured, semi-structured, and unstructured), and are stored in siloed systems, making cross-domain integration, real-time visualization, and advanced analytics nearly impossible. This creates a major bottleneck for marine scientists, conservationists, and policymakers who need to make data-driven decisions for ecosystem conservation and fisheries management.\n\nCMLRE seeks an AI-enabled, intelligent digital platform that can integrate heterogeneous and high-volume datasets from oceanography, taxonomy, morphology, and molecular biology into a single unified system. The platform should support automated data ingestion, standardisation, and metadata tagging using internationally accepted formats, enable cross-disciplinary correlation analysis to study how ocean parameters influence biodiversity, fish distribution, and ecosystem health, and incorporate interactive modules for otolith shape and morphometrics visualisation, taxonomic classification and species identification, and molecular and eDNA data storage/retrieval and species matching.\n\nWhy It Matters\n\nThis platform will serve as a national marine data backbone, empowering India's scientific community with a next-generation tool for holistic marine ecosystem assessment. It will improve the efficiency of marine biodiversity research, enhance real-time decision-making, and support blue economy initiatives by enabling sustainable fisheries and conservation planning through integrated data insights.\n\nExpected Deliverables\n\n• A robust, cloud-ready web platform prototype.\n• Scalable backend architecture with modular data ingestion pipelines.\n• Visualisation tools for oceanographic and biodiversity trends.\n• Integrated modules for managing taxonomy, otolith morphology, and eDNA data.\n• Well-documented APIs and user manuals for future adoption and scaling.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "About CMLRE\n\nCMLRE, Kochi, an attached office under the Ministry of Earth Sciences, plays a key role in organising and promoting ocean development activities in India, with a focus on mapping, assessing, and managing marine living resources in the Indian EEZ. The Marine Living Resources Programme supports ecosystem-based research and monitoring, aimed at sustainable utilisation of these resources through scientific surveys, environmental studies, and data-driven ecosystem modelling.",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Earth Sciences (MoES)",
"problem_description": "About CMLRE\n\nCMLRE, Kochi, an attached office under the Ministry of Earth Sciences, plays a key role in organising and promoting ocean development activities in India, with a focus on mapping, assessing, and managing marine living resources in the Indian EEZ. The Marine Living Resources Programme supports ecosystem-based research and monitoring, aimed at sustainable utilisation of these resources through scientific surveys, environmental studies, and data-driven ecosystem modelling.\n\nBackground\n\nIndia, with its vast coastline and rich marine biodiversity, relies heavily on the health of its ocean ecosystems for food security, climate regulation, and economic development. The Centre for Marine Living Resources and Ecology (CMLRE), under the Ministry of Earth Sciences, plays a pivotal role in collecting, managing, and analysing a broad spectrum of marine data to support sustainable ocean resource management.\n\nDetailed description\n\nCMLRE's datasets are diverse, complex, and multidisciplinary, including physical, chemical, and biological oceanography; fish abundance, species diversity, life history traits, and ecomorphology; fish taxonomy and otolith morphology; molecular biology data, including environmental DNA (eDNA) for species detection and biodiversity assessments. These datasets exist across various formats (structured, semi-structured, and unstructured), and are stored in siloed systems, making cross-domain integration, real-time visualization, and advanced analytics nearly impossible. This creates a major bottleneck for marine scientists, conservationists, and policymakers who need to make data-driven decisions for ecosystem conservation and fisheries management.\n\nCMLRE seeks an AI-enabled, intelligent digital platform that can integrate heterogeneous and high-volume datasets from oceanography, taxonomy, morphology, and molecular biology into a single unified system. The platform should support automated data ingestion, standardisation, and metadata tagging using internationally accepted formats, enable cross-disciplinary correlation analysis to study how ocean parameters influence biodiversity, fish distribution, and ecosystem health, and incorporate interactive modules for otolith shape and morphometrics visualisation, taxonomic classification and species identification, and molecular and eDNA data storage/retrieval and species matching.\n\nWhy It Matters\n\nThis platform will serve as a national marine data backbone, empowering India's scientific community with a next-generation tool for holistic marine ecosystem assessment. It will improve the efficiency of marine biodiversity research, enhance real-time decision-making, and support blue economy initiatives by enabling sustainable fisheries and conservation planning through integrated data insights.\n\nExpected Deliverables\n\n• A robust, cloud-ready web platform prototype.\n• Scalable backend architecture with modular data ingestion pipelines.\n• Visualisation tools for oceanographic and biodiversity trends.\n• Integrated modules for managing taxonomy, otolith morphology, and eDNA data.\n• Well-documented APIs and user manuals for future adoption and scaling.",
"ps_number": "SIH25041",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Renewable / Sustainable Energy",
"title": "AI-Driven Unified Data Platform for Oceanographic Fisheries and Molecular Biodiversity Insights"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "The deep ocean, encompassing vast and remote ecosystems like abyssal plains, hydrothermal vents, and seamounts, harbors a significant portion of global biodiversity, much of which remains undiscovered due to its inaccessibility. Understanding deep-sea biodiversity is critical for elucidating ecological interactions (e.g., food webs, nutrient cycling), informing conservation strategies for vulnerable marine habitats, and identifying novel eukaryotic species with potential biotechnological or ecological significance.\n\nEnvironmental DNA (eDNA) has emerged as a powerful, non-invasive tool for studying these ecosystems by capturing genetic traces of organisms from environmental samples, such as seawater or sediment, without the need for physical collection or disturbance of fragile habitats. By targeting marker genes like 18S rRNA or COI, eDNA enables the detection of diverse eukaryotic taxa, including protists, cnidarians, and rare metazoans, offering insights into species richness and community structure.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "The Centre for Marine Living Resources and Ecology (CMLRE) will undertake routine voyages to the deep sea and collect sediment and water samples from hotspot regions for biodiversity assessment and ecosystem monitoring. The water and sediment samples will be used to extract eDNA and will be subject to high-throughput sequencing.\n\nHowever, assigning raw eDNA sequencing reads to eukaryotic taxa or inferring their ecological roles presents significant challenges, primarily due to the poor representation of deep-sea organisms in reference databases like SILVA, PR2, or NCBI. These databases, built primarily from well-studied terrestrial or shallow-water species, lack comprehensive sequences for deep-sea eukaryotes, leading to misclassifications, unassigned reads, or underestimation of biodiversity.\n\nTraditional bioinformatic pipelines for eDNA analysis, such as those implemented in QIIME2, DADA2, or mothur, rely heavily on sequence alignment or mapping to these databases, which is inadequate for novel or divergent deep-sea taxa. This dependency limits the discovery of new species and hinders accurate biodiversity assessments, critical for conservation in rapidly changing deep-sea environments. The computational time required for processing eDNA data exacerbates these challenges, particularly given the limitations of database-dependent methods and the complexity of eDNA datasets.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "To address the challenges of poor database representation and computational time in deep-sea eDNA analysis, we propose an AI-driven pipeline that uses deep learning and unsupervised learning to identify eukaryotic taxa and assess biodiversity directly from raw eDNA reads. The solution should be able to classify the sequences, annotate and estimate abundance. This solution minimizes reliance on reference databases, reduces computational time through optimized workflows, and enables the discovery of novel taxa and ecological insights in deep-sea ecosystems.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Earth Sciences (MoES)",
"problem_description": "Background\n\nThe deep ocean, encompassing vast and remote ecosystems like abyssal plains, hydrothermal vents, and seamounts, harbors a significant portion of global biodiversity, much of which remains undiscovered due to its inaccessibility. Understanding deep-sea biodiversity is critical for elucidating ecological interactions (e.g., food webs, nutrient cycling), informing conservation strategies for vulnerable marine habitats, and identifying novel eukaryotic species with potential biotechnological or ecological significance.\n\nEnvironmental DNA (eDNA) has emerged as a powerful, non-invasive tool for studying these ecosystems by capturing genetic traces of organisms from environmental samples, such as seawater or sediment, without the need for physical collection or disturbance of fragile habitats. By targeting marker genes like 18S rRNA or COI, eDNA enables the detection of diverse eukaryotic taxa, including protists, cnidarians, and rare metazoans, offering insights into species richness and community structure.\n\nDescription\n\nThe Centre for Marine Living Resources and Ecology (CMLRE) will undertake routine voyages to the deep sea and collect sediment and water samples from hotspot regions for biodiversity assessment and ecosystem monitoring. The water and sediment samples will be used to extract eDNA and will be subject to high-throughput sequencing.\n\nHowever, assigning raw eDNA sequencing reads to eukaryotic taxa or inferring their ecological roles presents significant challenges, primarily due to the poor representation of deep-sea organisms in reference databases like SILVA, PR2, or NCBI. These databases, built primarily from well-studied terrestrial or shallow-water species, lack comprehensive sequences for deep-sea eukaryotes, leading to misclassifications, unassigned reads, or underestimation of biodiversity.\n\nTraditional bioinformatic pipelines for eDNA analysis, such as those implemented in QIIME2, DADA2, or mothur, rely heavily on sequence alignment or mapping to these databases, which is inadequate for novel or divergent deep-sea taxa. This dependency limits the discovery of new species and hinders accurate biodiversity assessments, critical for conservation in rapidly changing deep-sea environments. The computational time required for processing eDNA data exacerbates these challenges, particularly given the limitations of database-dependent methods and the complexity of eDNA datasets.\n\nExpected Solution\n\nTo address the challenges of poor database representation and computational time in deep-sea eDNA analysis, we propose an AI-driven pipeline that uses deep learning and unsupervised learning to identify eukaryotic taxa and assess biodiversity directly from raw eDNA reads. The solution should be able to classify the sequences, annotate and estimate abundance. This solution minimizes reliance on reference databases, reduces computational time through optimized workflows, and enables the discovery of novel taxa and ecological insights in deep-sea ecosystems.",
"ps_number": "SIH25042",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Identifying Taxonomy and Assessing Biodiversity from eDNA Datasets"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "Marine biodiversity assessments rely significantly on microscopic analysis of planktonic organisms, including phytoplankton, zooplankton, and other microscopic marine life. Traditionally, counting and species identification require manual microscopic examination, which is labor-intensive, subjective, and prone to human error. AI-enabled embedded microscopy systems provide an effective alternative by automating the identification, classification, and enumeration of these organisms directly at the point of sampling.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "This problem statement envisages developing an embedded intelligent system that can be integrated directly with microscopic imaging devices used in marine biological studies. The system should leverage embedding-based deep learning techniques to identify and count microscopic marine organisms captured through microscopy images. The embedded platform should be optimized for low computational resources, enabling accurate, rapid, and automated analysis without manual intervention.\n\nThe solution should specifically handle diverse challenges encountered in microscopic imaging, such as overlapping organisms, variations in morphology, illumination inconsistencies, and imaging artifacts. It must accurately classify and quantify different species present in microscopic images to facilitate marine biodiversity assessments efficiently.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "An embedded microscopy-integrated AI system featuring:\n• Lightweight deep learning models deployable on embedded computational hardware.\n• Automatic species/genus-level identification of microscopic marine organisms from microscopy images.\n• Accurate and automated counting of individual organisms per species.\n• Capability for onboard processing, storage, and reporting of species identification and counts for subsequent ecological analysis.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Earth Sciences (MoES)",
"problem_description": "Background\n\nMarine biodiversity assessments rely significantly on microscopic analysis of planktonic organisms, including phytoplankton, zooplankton, and other microscopic marine life. Traditionally, counting and species identification require manual microscopic examination, which is labor-intensive, subjective, and prone to human error. AI-enabled embedded microscopy systems provide an effective alternative by automating the identification, classification, and enumeration of these organisms directly at the point of sampling.\n\nDescription\n\nThis problem statement envisages developing an embedded intelligent system that can be integrated directly with microscopic imaging devices used in marine biological studies. The system should leverage embedding-based deep learning techniques to identify and count microscopic marine organisms captured through microscopy images. The embedded platform should be optimized for low computational resources, enabling accurate, rapid, and automated analysis without manual intervention.\n\nThe solution should specifically handle diverse challenges encountered in microscopic imaging, such as overlapping organisms, variations in morphology, illumination inconsistencies, and imaging artifacts. It must accurately classify and quantify different species present in microscopic images to facilitate marine biodiversity assessments efficiently.\n\nExpected Solution\n\nAn embedded microscopy-integrated AI system featuring:\n• Lightweight deep learning models deployable on embedded computational hardware.\n• Automatic species/genus-level identification of microscopic marine organisms from microscopy images.\n• Accurate and automated counting of individual organisms per species.\n• Capability for onboard processing, storage, and reporting of species identification and counts for subsequent ecological analysis.",
"ps_number": "SIH25043",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "Embedded Intelligent Microscopy System for Identification and Counting of Microscopic Marine Organisms"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Develop an AI-based platform to predict crop yields using historical agricultural data, weather patterns, and soil health metrics. The system should provide actionable recommendations for farmers to optimize irrigation, fertilization, and pest control, tailored to specific crops and regional conditions.\n\nExpected Outcome\n\nA scalable software solution (web/mobile app) that helps small-scale farmers increase productivity by at least 10% through data-driven insights, with an interface supporting regional languages for accessibility.\n\nTechnical Feasibility\n\nUtilizes machine learning models (e.g., regression, neural networks) trained on open-source agricultural datasets, integrated with APIs for real-time weather and soil data.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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},
"organization": "Government of Odisha",
"problem_description": "Description\n\nDevelop an AI-based platform to predict crop yields using historical agricultural data, weather patterns, and soil health metrics. The system should provide actionable recommendations for farmers to optimize irrigation, fertilization, and pest control, tailored to specific crops and regional conditions.\n\nExpected Outcome\n\nA scalable software solution (web/mobile app) that helps small-scale farmers increase productivity by at least 10% through data-driven insights, with an interface supporting regional languages for accessibility.\n\nTechnical Feasibility\n\nUtilizes machine learning models (e.g., regression, neural networks) trained on open-source agricultural datasets, integrated with APIs for real-time weather and soil data.",
"ps_number": "SIH25044",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "AI-Powered Crop Yield Prediction and Optimization"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
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"deliverables": null,
"description": "Create a blockchain-based system to track agricultural produce from farm to consumer, ensuring transparency in pricing, quality, and origin. The solution should allow stakeholders (farmers, distributors, retailers) to verify transactions and reduce exploitation in the supply chain.\n\nExpected Outcome\n\nA decentralized platform with a user-friendly interface for farmers and consumers to trace produce, reducing fraud and ensuring fair pricing, deployable on low-cost hardware or cloud infrastructure.\n\nTechnical Feasibility\n\nLeverages existing blockchain frameworks like Ethereum or Hyperledger, with smart contracts for automated tracking and QR code integration for consumer access.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
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"impact": null,
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"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Odisha",
"problem_description": "Description\n\nCreate a blockchain-based system to track agricultural produce from farm to consumer, ensuring transparency in pricing, quality, and origin. The solution should allow stakeholders (farmers, distributors, retailers) to verify transactions and reduce exploitation in the supply chain.\n\nExpected Outcome\n\nA decentralized platform with a user-friendly interface for farmers and consumers to trace produce, reducing fraud and ensuring fair pricing, deployable on low-cost hardware or cloud infrastructure.\n\nTechnical Feasibility\n\nLeverages existing blockchain frameworks like Ethereum or Hyperledger, with smart contracts for automated tracking and QR code integration for consumer access.",
"ps_number": "SIH25045",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "Blockchain-Based Supply Chain Transparency for Agricultural Produce"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Design an IoT-enabled waste segregation system using sensors and machine learning to automatically classify household waste (organic, recyclable, hazardous) at the source. The system should integrate with municipal waste management for efficient collection and recycling.\n\nExpected Outcome\n\nA prototype device with 90% accuracy in waste classification, coupled with a mobile app for households to monitor waste disposal and earn incentives for recycling.\n\nTechnical Feasibility\n\nUses affordable sensors (e.g., cameras, weight sensors) and ML models (e.g., convolutional neural networks) for waste identification, deployable in urban and semi-urban areas.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
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"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Odisha",
"problem_description": "Description\n\nDesign an IoT-enabled waste segregation system using sensors and machine learning to automatically classify household waste (organic, recyclable, hazardous) at the source. The system should integrate with municipal waste management for efficient collection and recycling.\n\nExpected Outcome\n\nA prototype device with 90% accuracy in waste classification, coupled with a mobile app for households to monitor waste disposal and earn incentives for recycling.\n\nTechnical Feasibility\n\nUses affordable sensors (e.g., cameras, weight sensors) and ML models (e.g., convolutional neural networks) for waste identification, deployable in urban and semi-urban areas.",
"ps_number": "SIH25046",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Clean & Green Technology",
"title": "Smart Waste Segregation and Recycling System"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Develop a drone-based system to deliver medical supplies and communication devices to remote areas during natural disasters (floods, earthquakes). The drone should use AI for real-time navigation and obstacle avoidance, with a payload capacity of at least 5 kg.\n\nExpected Outcome\n\nA working drone prototype with a mobile app for disaster management teams to coordinate deliveries, reducing response time by 20% in inaccessible regions.\n\nTechnical Feasibility\n\nEmploys existing drone hardware with open-source flight control software (e.g., Ardupilot) and AI algorithms for navigation, tested in simulated disaster scenarios.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Odisha",
"problem_description": "Description\n\nDevelop a drone-based system to deliver medical supplies and communication devices to remote areas during natural disasters (floods, earthquakes). The drone should use AI for real-time navigation and obstacle avoidance, with a payload capacity of at least 5 kg.\n\nExpected Outcome\n\nA working drone prototype with a mobile app for disaster management teams to coordinate deliveries, reducing response time by 20% in inaccessible regions.\n\nTechnical Feasibility\n\nEmploys existing drone hardware with open-source flight control software (e.g., Ardupilot) and AI algorithms for navigation, tested in simulated disaster scenarios.",
"ps_number": "SIH25047",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Robotics and Drones",
"title": "Disaster Response Drone for Remote Areas"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Build a gamified digital platform to enhance learning outcomes for students in rural schools (grades 6-12), focusing on STEM subjects. The platform should use interactive games, multilingual content, and offline access to engage students with limited internet connectivity.\n\nExpected Outcome\n\nA mobile app or web platform increasing student engagement by 15%, with analytics for teachers to track progress, deployable on low-cost devices.\n\nTechnical Feasibility\n\nUses HTML5/CSS for offline-compatible web apps, integrated with open-source gamification frameworks and localized content delivery.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Odisha",
"problem_description": "Description\n\nBuild a gamified digital platform to enhance learning outcomes for students in rural schools (grades 6-12), focusing on STEM subjects. The platform should use interactive games, multilingual content, and offline access to engage students with limited internet connectivity.\n\nExpected Outcome\n\nA mobile app or web platform increasing student engagement by 15%, with analytics for teachers to track progress, deployable on low-cost devices.\n\nTechnical Feasibility\n\nUses HTML5/CSS for offline-compatible web apps, integrated with open-source gamification frameworks and localized content delivery.",
"ps_number": "SIH25048",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Gamified Learning Platform for Rural Education"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Create a multilingual AI chatbot to educate rural and semi-urban populations about preventive healthcare, disease symptoms, and vaccination schedules. The chatbot should integrate with government health databases and provide real-time alerts for outbreaks.\n\nExpected Outcome\n\nA chatbot accessible via WhatsApp or SMS, reaching 80% accuracy in answering health queries and increasing awareness by 20% in target communities.\n\nTechnical Feasibility\n\nBuilt using NLP frameworks (e.g., Rasa, Dialogflow) with APIs for health data integration, deployable on cloud platforms for scalability.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Odisha",
"problem_description": "Description\n\nCreate a multilingual AI chatbot to educate rural and semi-urban populations about preventive healthcare, disease symptoms, and vaccination schedules. The chatbot should integrate with government health databases and provide real-time alerts for outbreaks.\n\nExpected Outcome\n\nA chatbot accessible via WhatsApp or SMS, reaching 80% accuracy in answering health queries and increasing awareness by 20% in target communities.\n\nTechnical Feasibility\n\nBuilt using NLP frameworks (e.g., Rasa, Dialogflow) with APIs for health data integration, deployable on cloud platforms for scalability.",
"ps_number": "SIH25049",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "MedTech / BioTech / HealthTech",
"title": "AI-Driven Public Health Chatbot for Disease Awareness"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Design an AI-based traffic management system to optimize signal timings and reduce congestion in urban areas. The system should analyze real-time traffic data from cameras and IoT sensors to predict and mitigate bottlenecks.\n\nExpected Outcome\n\nA software prototype reducing average commute time by 10% in a simulated urban environment, with a dashboard for traffic authorities to monitor and control signals.\n\nTechnical Feasibility\n\nUses computer vision (e.g., OpenCV) and reinforcement learning for traffic prediction, integrated with existing traffic camera networks.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Odisha",
"problem_description": "Description\n\nDesign an AI-based traffic management system to optimize signal timings and reduce congestion in urban areas. The system should analyze real-time traffic data from cameras and IoT sensors to predict and mitigate bottlenecks.\n\nExpected Outcome\n\nA software prototype reducing average commute time by 10% in a simulated urban environment, with a dashboard for traffic authorities to monitor and control signals.\n\nTechnical Feasibility\n\nUses computer vision (e.g., OpenCV) and reinforcement learning for traffic prediction, integrated with existing traffic camera networks.",
"ps_number": "SIH25050",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Transportation & Logistics",
"title": "Smart Traffic Management System for Urban Congestion"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Develop an IoT-based monitoring system for solar or wind microgrids in rural areas, providing real-time data on energy generation, storage, and consumption. The system should alert users to inefficiencies or maintenance needs.\n\nExpected Outcome\n\nA hardware-software prototype improving microgrid efficiency by 15%, with a mobile app for community operators to manage energy distribution.\n\nTechnical Feasibility\n\nEmploys affordable IoT sensors (e.g., Raspberry Pi, Arduino) and cloud-based analytics for real-time monitoring, compatible with existing microgrid setups.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Odisha",
"problem_description": "Description\n\nDevelop an IoT-based monitoring system for solar or wind microgrids in rural areas, providing real-time data on energy generation, storage, and consumption. The system should alert users to inefficiencies or maintenance needs.\n\nExpected Outcome\n\nA hardware-software prototype improving microgrid efficiency by 15%, with a mobile app for community operators to manage energy distribution.\n\nTechnical Feasibility\n\nEmploys affordable IoT sensors (e.g., Raspberry Pi, Arduino) and cloud-based analytics for real-time monitoring, compatible with existing microgrid setups.",
"ps_number": "SIH25051",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Renewable / Sustainable Energy",
"title": "Renewable Energy Monitoring System for Microgrids"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Create an augmented reality (AR) platform to digitize and preserve India's cultural heritage sites, allowing users to experience virtual tours with historical narratives. The platform should support low-bandwidth access for rural users.\n\nExpected Outcome\n\nAn AR app with 3D models of at least five heritage sites, increasing tourist engagement by 10% and promoting cultural education.\n\nTechnical Feasibility\n\nUses AR frameworks (e.g., Unity, ARCore) with compressed 3D models for low-bandwidth compatibility, hosted on cloud servers.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Odisha",
"problem_description": "Description\n\nCreate an augmented reality (AR) platform to digitize and preserve India's cultural heritage sites, allowing users to experience virtual tours with historical narratives. The platform should support low-bandwidth access for rural users.\n\nExpected Outcome\n\nAn AR app with 3D models of at least five heritage sites, increasing tourist engagement by 10% and promoting cultural education.\n\nTechnical Feasibility\n\nUses AR frameworks (e.g., Unity, ARCore) with compressed 3D models for low-bandwidth compatibility, hosted on cloud servers.",
"ps_number": "SIH25052",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Heritage & Culture",
"title": "AR-Based Cultural Heritage Preservation Platform"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "The majority of the farmers in India store the onion in naturally ventilated storage structures. In such structures, onions are exposed to extreme temperatures and relative humidity, which reduces the shelf life of onions and high storage loss. Improved onion storage technology will help in ensuring the availability of onions between seasons and obviate seasonal volatility in prices. The solutions could be in the form of improved, but cost-effective, materials and design of storage structures to manage temperature and relative humidity.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Onion is a semi-perishable vegetable and is harvested during the rabi season, accounting for 65% of onion production, and hits the markets from April to May. The same crop must continue to meet the consumer demand till the month of October-November every year, before the kharif crop is harvested and brought to the market. It is therefore vital to store onions successfully in order to meet the regular supply. It is observed that nearly 30-40% of the crop is lost during storage due to various reasons in form of physiological weight loss, rotting, sprouting etc. In unexpected situations such as natural calamities, the losses even go beyond 50% creating heavy stress both on the demand and supply sides. The losses that occurred during storage are in terms of qualitative as well as in quantitative ways. Hence, it is imperative to take some crucial steps pertaining to onion storage with minimum losses to ensure an adequate supply to the market, thereby reducing the price fluctuations.\n\nVarious abiotic factors like temperature, relative humidity affect the health of onions; hence, their balance is essential to store the crop with minimum losses. Recent technological advancements (such as Design, Solar/Plasma/Irradiation energy, Nanotechnology, IoT including AI/ML) also offer an opportunity to provide infrastructure for onion storage and supply chain, besides value addition, even in rural India. Design innovations are pervading various sectors, including efficient onion storage at the farm level, rural as well as urban markets. Different process and product development advances will fetch better returns to farmers and competitive prices for consumers. More importantly, it will help in addressing the present as well as the impending challenges.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "Extension of shelf life with minimum loss.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Consumer Affairs Food & Public Distribution (MoCA F&PD)",
"problem_description": "Background\n\nThe majority of the farmers in India store the onion in naturally ventilated storage structures. In such structures, onions are exposed to extreme temperatures and relative humidity, which reduces the shelf life of onions and high storage loss. Improved onion storage technology will help in ensuring the availability of onions between seasons and obviate seasonal volatility in prices. The solutions could be in the form of improved, but cost-effective, materials and design of storage structures to manage temperature and relative humidity.\n\nDescription\n\nOnion is a semi-perishable vegetable and is harvested during the rabi season, accounting for 65% of onion production, and hits the markets from April to May. The same crop must continue to meet the consumer demand till the month of October-November every year, before the kharif crop is harvested and brought to the market. It is therefore vital to store onions successfully in order to meet the regular supply. It is observed that nearly 30-40% of the crop is lost during storage due to various reasons in form of physiological weight loss, rotting, sprouting etc. In unexpected situations such as natural calamities, the losses even go beyond 50% creating heavy stress both on the demand and supply sides. The losses that occurred during storage are in terms of qualitative as well as in quantitative ways. Hence, it is imperative to take some crucial steps pertaining to onion storage with minimum losses to ensure an adequate supply to the market, thereby reducing the price fluctuations.\n\nVarious abiotic factors like temperature, relative humidity affect the health of onions; hence, their balance is essential to store the crop with minimum losses. Recent technological advancements (such as Design, Solar/Plasma/Irradiation energy, Nanotechnology, IoT including AI/ML) also offer an opportunity to provide infrastructure for onion storage and supply chain, besides value addition, even in rural India. Design innovations are pervading various sectors, including efficient onion storage at the farm level, rural as well as urban markets. Different process and product development advances will fetch better returns to farmers and competitive prices for consumers. More importantly, it will help in addressing the present as well as the impending challenges.\n\nExpected Solution\n\nExtension of shelf life with minimum loss.",
"ps_number": "SIH25053",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "Improved Onion storage technology for enhancing shelf life of onions"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "The safety of electrical installations hinges on reliable Miniature Circuit Breakers (MCBs). IEC 60898-1:2015 mandates rigorous short-circuit breaking capacity tests, crucial for ensuring MCBs perform correctly under severe fault conditions.\n\nExisting Problem\n\nCurrent manual or semi-automated testing methods for MCBs introduce significant challenges. These include imprecise R (resistive) and XL (inductive) circuit configurations, increased test times, and elevated safety risks for personnel during high-energy fault current generation (up to 10,000A). This impacts test accuracy, repeatability, and overall safety in the MCB certification process.\n\nDetailed Description\n\nThis proposal outlines an automated machine to precisely control test currents, voltages, and circuit impedance, executing high-current short-circuit tests on single pole, SPN, DP, TP, and FP MCBs (0.5A-63A) per IEC 60898-1:2015. It features an Automated R and XL Circuit Combination Module with high-power, automatically switched banks for precise power factor control. A High-Current Power Source (transformer-based) delivers up to 10,000A. The Test Station includes universal MCB mounting and a critical arc chute for safety. A sophisticated Control and Data Acquisition System (PLC/Industrial PC) manages tests, captures high-speed waveforms, and analyzes data. A user-friendly HMI allows parameter input and automatic report generation. Comprehensive safety systems are integrated.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "The automated machine will perform MCB breaking capacity tests with unprecedented accuracy and repeatability, fully adhering to IEC 60898-1:2015. This automation will ensure precise parameter control, significantly reduce test times, and enhance safety by minimizing human intervention during high-energy fault conditions. This state-of-the-art facility will provide a reliable platform for MCB certification, contributing directly to electrical safety and quality assurance.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Consumer Affairs Food & Public Distribution (MoCA F&PD)",
"problem_description": "Background\n\nThe safety of electrical installations hinges on reliable Miniature Circuit Breakers (MCBs). IEC 60898-1:2015 mandates rigorous short-circuit breaking capacity tests, crucial for ensuring MCBs perform correctly under severe fault conditions.\n\nExisting Problem\n\nCurrent manual or semi-automated testing methods for MCBs introduce significant challenges. These include imprecise R (resistive) and XL (inductive) circuit configurations, increased test times, and elevated safety risks for personnel during high-energy fault current generation (up to 10,000A). This impacts test accuracy, repeatability, and overall safety in the MCB certification process.\n\nDetailed Description\n\nThis proposal outlines an automated machine to precisely control test currents, voltages, and circuit impedance, executing high-current short-circuit tests on single pole, SPN, DP, TP, and FP MCBs (0.5A-63A) per IEC 60898-1:2015. It features an Automated R and XL Circuit Combination Module with high-power, automatically switched banks for precise power factor control. A High-Current Power Source (transformer-based) delivers up to 10,000A. The Test Station includes universal MCB mounting and a critical arc chute for safety. A sophisticated Control and Data Acquisition System (PLC/Industrial PC) manages tests, captures high-speed waveforms, and analyzes data. A user-friendly HMI allows parameter input and automatic report generation. Comprehensive safety systems are integrated.\n\nExpected Solution\n\nThe automated machine will perform MCB breaking capacity tests with unprecedented accuracy and repeatability, fully adhering to IEC 60898-1:2015. This automation will ensure precise parameter control, significantly reduce test times, and enhance safety by minimizing human intervention during high-energy fault conditions. This state-of-the-art facility will provide a reliable platform for MCB certification, contributing directly to electrical safety and quality assurance.",
"ps_number": "SIH25054",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "Automated High-Current Short-Circuit Test System for MCB to comply with IEC 60898-1:2015"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "Accurate and consistent preparation of cable specimens is vital for reliable testing according to Indian Standards like IS 10810 (Parts 2, 7, 33) and IS 7098 (Parts 1 & 2). These tests, including conductor resistance, insulation/sheath thickness, and flame retardance, are crucial for ensuring cable safety and quality.\n\nExisting Problem\n\nCurrently, cable sample preparation involves significant manual intervention. The cable sample is manually cut by the operator and then straightened manually. Subsequently, these straightened PVC/XLPE/HDPE cable samples are cut into slices using electrically/pneumatically operated machines. Further, the samples are shaped as dumbbells by a dumbbell cutting machine. These manual and semi-automated steps are time-consuming, prone to human error, and introduce inconsistencies that compromise the accuracy and repeatability of critical test results.\n\nDetailed Description\n\nThis project develops an automated machine designed to precisely cut insulation and outer sheaths from cables, preparing specimens that strictly adhere to the aforementioned IS standards. Key features include an Automated Cable Feeding and Clamping System utilizing motor-driven rollers and adjustable clamps for secure, straightened cable handling. The Cutting and Stripping Module employs precision blades with programmable depths for clean, circumferential cuts and linear stripping, fulfilling specific length requirements for various tests. An integrated diameter sensor will auto-adjust settings. A Control System (PLC/HMI) manages operations, allows test method selection, monitors status, and provides closed-loop feedback. Automated specimen ejection and waste management further streamline workflow. Robust safety features like enclosed areas and interlocks will protect operators.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "The automated machine will eliminate human error, guaranteeing consistent and repeatable specimen quality while significantly reducing preparation time and costs. By ensuring strict adherence to IS standards, the solution will yield more reliable test results and simplify product certification. This advancement will markedly improve accuracy, efficiency, and safety in cable testing, supporting high-quality control in the cable manufacturing industry.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Consumer Affairs Food & Public Distribution (MoCA F&PD)",
"problem_description": "Background\n\nAccurate and consistent preparation of cable specimens is vital for reliable testing according to Indian Standards like IS 10810 (Parts 2, 7, 33) and IS 7098 (Parts 1 & 2). These tests, including conductor resistance, insulation/sheath thickness, and flame retardance, are crucial for ensuring cable safety and quality.\n\nExisting Problem\n\nCurrently, cable sample preparation involves significant manual intervention. The cable sample is manually cut by the operator and then straightened manually. Subsequently, these straightened PVC/XLPE/HDPE cable samples are cut into slices using electrically/pneumatically operated machines. Further, the samples are shaped as dumbbells by a dumbbell cutting machine. These manual and semi-automated steps are time-consuming, prone to human error, and introduce inconsistencies that compromise the accuracy and repeatability of critical test results.\n\nDetailed Description\n\nThis project develops an automated machine designed to precisely cut insulation and outer sheaths from cables, preparing specimens that strictly adhere to the aforementioned IS standards. Key features include an Automated Cable Feeding and Clamping System utilizing motor-driven rollers and adjustable clamps for secure, straightened cable handling. The Cutting and Stripping Module employs precision blades with programmable depths for clean, circumferential cuts and linear stripping, fulfilling specific length requirements for various tests. An integrated diameter sensor will auto-adjust settings. A Control System (PLC/HMI) manages operations, allows test method selection, monitors status, and provides closed-loop feedback. Automated specimen ejection and waste management further streamline workflow. Robust safety features like enclosed areas and interlocks will protect operators.\n\nExpected Solution\n\nThe automated machine will eliminate human error, guaranteeing consistent and repeatable specimen quality while significantly reducing preparation time and costs. By ensuring strict adherence to IS standards, the solution will yield more reliable test results and simplify product certification. This advancement will markedly improve accuracy, efficiency, and safety in cable testing, supporting high-quality control in the cable manufacturing industry.",
"ps_number": "SIH25055",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Automated Specimen Preparation System for testing of Cable samples as per IS 10810 and IS 7098"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
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"introduction": "Background:\nHallmarking is the accurate determination and official recording of the proportionate content of precious metal in the jewellery/artefacts or bullion/coins. The hallmarking of gold jewellery was started in year 2000 and has been made mandatory since 23 June 2021 and presently covers 361 districts.\n\nThe Indian Standard, IS 1417 2016 \"Gold and gold alloys, jewellery/artefacts-fineness and marking specification\" covers hallmarking of six grades of gold jewellery/gold artefacts viz. 14K, 18K, 20K, 22K, 23K and 24K carats. The Indian Standard, IS 1418: 2009 \"Determination of gold in gold bullion, gold alloys and gold jewellery/artefacts - Cupellation (Fire Assay) method\" prescribes the fire assay test method for determining the fineness of gold in gold bullion, alloys, and jewellery/artefacts.\n\nThe fire assay testing method, though globally accepted and accurate, involves a destructive testing process wherein a sample of 250-500 mg is taken from the item by scrapping, drilling, or cutting. The method may lead to the damage of the article, rendering it unsellable, especially when dealing with a few or single article. However, the gold loss incurred during testing is almost negligible. Additionally, the process emits hazardous substances like lead oxides and nitrous fumes.\n\nFurther, BIS has also formulated an Indian Standard on method of test using ED-XRF (Energy Dispersive X-Ray Fluorescence), i.e., IS 18458: 2023 \"Jewellery and precious metals ED-XRF test - Method of test\". This Indian Standard prescribes a non-destructive testing method to verify the fineness of the gold jewellery. However, the use of ED-XRF as a non-destructive method to determine the fineness is only applicable to homogenous gold jewellery. In addition, IS 18458: 2023 also has a provision for determination of fineness of jewellery by destructive testing, which is more suited for non-homogeneous jewellery.\n\nThere is another Indian Standard developed by BIS, IS 16901: 2022 \"Jewellery and precious metals determination of high purity gold, platinum and palladium - difference method using ICP-OES\". This method uses ICP-OES and is applicable only for high purity gold (999 ppt and above). ICP-OES is also a destructive testing method.\n\nIndia's gold jewellery industry is highly diverse, encompassing various types such as casted jewellery, handmade pieces with soldered joints, hollow and filled jewellery, gold-plated items, and studded ornaments using various jewellery manufacturing techniques. While casted jewellery is relatively homogeneous, the majority-particularly handmade and hollow varieties-are structurally complex and heterogeneous. This variability presents significant challenges in testing and hallmarking.\n\nIn the existing fire assay testing method, heterogeneity is addressed by melting the sample before testing. This eliminates impurities and ensures homogeneity. Moreover, fire assay being a difference method (based on initial and final weight of gold sample), the impact of heterogeneity is nullified. However, owing to its destructive nature, there is strong demand for an alternative non-destructive testing method.\n\nExpected Solution:\na. Develop and implement a non-destructive assaying method that matches the accuracy of fire assay without damaging the jewellery, while being capable of handling heterogeneous compositions (solders, fillers, non-metallic substances).\nb. Eliminate emissions of harmful substances (lead oxide, nitrous fumes).\nc. Achieve repeatability close to that of fire assay, ideally within ±0.5 ppt (as per IS 1418).\nd. Method selected will be validated for regulatory and commercial applications.\n\nDeliverables:\na. Detailed non-destructive innovative testing method with technical explanation\nb. References/bibliography related to the technical paper\nc. Scientific principles underlying the approach\nd. Feasibility studies and expected challenges\ne. Methodology for validation including accuracy assessment\nf. Explanatory note on potential for regulatory and commercial application",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
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"multilingual_support": null,
"objective": null,
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"relevant_stakeholders___beneficiaries": null,
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},
"organization": "Ministry of Consumer Affairs Food & Public Distribution (MoCA F&PD)",
"problem_description": "Background:\nHallmarking is the accurate determination and official recording of the proportionate content of precious metal in the jewellery/artefacts or bullion/coins. The hallmarking of gold jewellery was started in year 2000 and has been made mandatory since 23 June 2021 and presently covers 361 districts.\n\nThe Indian Standard, IS 1417 2016 \"Gold and gold alloys, jewellery/artefacts-fineness and marking specification\" covers hallmarking of six grades of gold jewellery/gold artefacts viz. 14K, 18K, 20K, 22K, 23K and 24K carats. The Indian Standard, IS 1418: 2009 \"Determination of gold in gold bullion, gold alloys and gold jewellery/artefacts - Cupellation (Fire Assay) method\" prescribes the fire assay test method for determining the fineness of gold in gold bullion, alloys, and jewellery/artefacts.\n\nThe fire assay testing method, though globally accepted and accurate, involves a destructive testing process wherein a sample of 250-500 mg is taken from the item by scrapping, drilling, or cutting. The method may lead to the damage of the article, rendering it unsellable, especially when dealing with a few or single article. However, the gold loss incurred during testing is almost negligible. Additionally, the process emits hazardous substances like lead oxides and nitrous fumes.\n\nFurther, BIS has also formulated an Indian Standard on method of test using ED-XRF (Energy Dispersive X-Ray Fluorescence), i.e., IS 18458: 2023 \"Jewellery and precious metals ED-XRF test - Method of test\". This Indian Standard prescribes a non-destructive testing method to verify the fineness of the gold jewellery. However, the use of ED-XRF as a non-destructive method to determine the fineness is only applicable to homogenous gold jewellery. In addition, IS 18458: 2023 also has a provision for determination of fineness of jewellery by destructive testing, which is more suited for non-homogeneous jewellery.\n\nThere is another Indian Standard developed by BIS, IS 16901: 2022 \"Jewellery and precious metals determination of high purity gold, platinum and palladium - difference method using ICP-OES\". This method uses ICP-OES and is applicable only for high purity gold (999 ppt and above). ICP-OES is also a destructive testing method.\n\nIndia's gold jewellery industry is highly diverse, encompassing various types such as casted jewellery, handmade pieces with soldered joints, hollow and filled jewellery, gold-plated items, and studded ornaments using various jewellery manufacturing techniques. While casted jewellery is relatively homogeneous, the majority-particularly handmade and hollow varieties-are structurally complex and heterogeneous. This variability presents significant challenges in testing and hallmarking.\n\nIn the existing fire assay testing method, heterogeneity is addressed by melting the sample before testing. This eliminates impurities and ensures homogeneity. Moreover, fire assay being a difference method (based on initial and final weight of gold sample), the impact of heterogeneity is nullified. However, owing to its destructive nature, there is strong demand for an alternative non-destructive testing method.\n\nExpected Solution:\na. Develop and implement a non-destructive assaying method that matches the accuracy of fire assay without damaging the jewellery, while being capable of handling heterogeneous compositions (solders, fillers, non-metallic substances).\nb. Eliminate emissions of harmful substances (lead oxide, nitrous fumes).\nc. Achieve repeatability close to that of fire assay, ideally within ±0.5 ppt (as per IS 1418).\nd. Method selected will be validated for regulatory and commercial applications.\n\nDeliverables:\na. Detailed non-destructive innovative testing method with technical explanation\nb. References/bibliography related to the technical paper\nc. Scientific principles underlying the approach\nd. Feasibility studies and expected challenges\ne. Methodology for validation including accuracy assessment\nf. Explanatory note on potential for regulatory and commercial application",
"ps_number": "SIH25056",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "To explore new/alternative assaying methods to the fire assay method for testing of gold jewellery and artefacts through non-destructive testing"
}
|
{
"category": "Hardware",
"details": {
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"conclusion": null,
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"introduction": "Background:\nWith the exponential growth of e-commerce in India and globally, the need to ensure accurate and legally compliant declarations on product listings has become more critical than ever. Under the Legal Metrology (Packaged Commodities) Rules, 2011, all pre-packaged goods sold online must clearly and accurately display information such as:\n- Name and address of the manufacturer, packer/importer\n- Net quantity in terms of standard units of weight, measures or number\n- Retail sale price (MRP) inclusive of all taxes\n- Consumer care details\n- Date of manufacture/import\n- Country of origin\n\nDespite regulatory efforts, non-compliance on digital platforms remains widespread due to various reasons. To address this, the Department envisions a smart, scalable and automated solution that can verify declarations in real-time across various e-commerce websites.\n\nProblem Description:\nDevelop an AI-powered automated compliance checker that can scan e-commerce platforms for product listings and validate them against mandatory Legal Metrology requirements. The system should:\n- Extract text from product images using OCR (Optical Character Recognition)\n- Crawl and scrape data from product listings in real time or batch mode\n- Validate the information against a customizable rule engine built on Legal Metrology guidelines\n- Flag non-compliance issues (e.g., missing MRP, incorrect unit, misleading quantity, absence of country of origin, etc.)\n- Provide a dashboard/reporting tool for regulators to monitor violations and track compliance trends\n\nKey Functional Requirements:\nData Acquisition:\n- Web crawling APIs for major e-commerce platforms (Amazon, Flipkart, etc.)\n- Image recognition to identify and crop relevant label regions\n\nOCR & AI:\n- Multi-language OCR support for label text extraction\n- Use of computer vision to segment declarations on packaging\n\nRule Engine:\n- Logical engine to validate each extracted field as per Packaged Commodities Rules\n- Configurable for rule updates and regional variations\n\nDashboard:\n- Visual dashboard for regulators showing:\n • Real-time compliance score\n • Trends by category, brand, or seller\n • Exportable violation reports\n • Geo-tagged compliance heatmaps (if applicable)\n\nScalability & Security:\n- Cloud-based architecture for large-scale crawling\n- Secure data logging and access control for regulators\n\nTarget Participants:\n- Final year UG (B.Tech/B.E.) students in Electronics, Embedded Systems, Instrumentation, AI/ML, Cybersecurity\n- PG/Ph.D. researchers in IoT Security, Metrology, Data Forensics\n- Startups or academia-industry teams working on compliance technologies\n\nEvaluation Criteria:\n- Innovation (Use of novel AI/ML/NLP or scraping techniques)\n- Accuracy (Precision in identifying compliant and non-compliant listings)\n- Scalability (Performance on large product databases)\n- User Experience (Ease of dashboard, actionable reports)\n- Alignment with Legal Metrology Rules\n- Regulatory Applicability (Can be deployed by Govt. departments with minimal changes)\n\nExpected Deliverables:\n(a) A working prototype of the compliance checker tool (Web-based or standalone app)\n(b) Technical documentation including:\n • Data pipeline design\n • OCR and ML models used\n • Validation methodology\n(c) Sample dataset with annotation (compliant/non-compliant examples)\n(d) Dashboard demo with live/test crawl outputs\n(e) Feasibility report for deployment with government bodies\n\nBenefits and Impact:\n- Saves time and effort in manual inspection of product declarations\n- Promotes fair trade and protects consumers from deceptive practices\n- Enables data-driven enforcement of Legal Metrology Act\n- Prepares India for next-gen regulation of digital marketplaces\n\nMentorship and Resources:\n- Legal Metrology Act and Rules/guidelines/advisories\n- Access to curated datasets of product images and declarations\n- Webinars by domain experts from the Ministry/Legal Metrology Division\n- Access to sandbox e-commerce API for testing solutions\n\nFuture Prospects:\n- Integration with a national regulatory enforcement portal\n- Use as a plug-in for e-commerce portals for real-time seller feedback\n- The scheme may be evaluated by the officers IILM, Ranchi, RRSLs and LM Hq, Delhi",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
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},
"organization": "Ministry of Consumer Affairs Food & Public Distribution (MoCA F&PD)",
"problem_description": "Background:\nWith the exponential growth of e-commerce in India and globally, the need to ensure accurate and legally compliant declarations on product listings has become more critical than ever. Under the Legal Metrology (Packaged Commodities) Rules, 2011, all pre-packaged goods sold online must clearly and accurately display information such as:\n- Name and address of the manufacturer, packer/importer\n- Net quantity in terms of standard units of weight, measures or number\n- Retail sale price (MRP) inclusive of all taxes\n- Consumer care details\n- Date of manufacture/import\n- Country of origin\n\nDespite regulatory efforts, non-compliance on digital platforms remains widespread due to various reasons. To address this, the Department envisions a smart, scalable and automated solution that can verify declarations in real-time across various e-commerce websites.\n\nProblem Description:\nDevelop an AI-powered automated compliance checker that can scan e-commerce platforms for product listings and validate them against mandatory Legal Metrology requirements. The system should:\n- Extract text from product images using OCR (Optical Character Recognition)\n- Crawl and scrape data from product listings in real time or batch mode\n- Validate the information against a customizable rule engine built on Legal Metrology guidelines\n- Flag non-compliance issues (e.g., missing MRP, incorrect unit, misleading quantity, absence of country of origin, etc.)\n- Provide a dashboard/reporting tool for regulators to monitor violations and track compliance trends\n\nKey Functional Requirements:\nData Acquisition:\n- Web crawling APIs for major e-commerce platforms (Amazon, Flipkart, etc.)\n- Image recognition to identify and crop relevant label regions\n\nOCR & AI:\n- Multi-language OCR support for label text extraction\n- Use of computer vision to segment declarations on packaging\n\nRule Engine:\n- Logical engine to validate each extracted field as per Packaged Commodities Rules\n- Configurable for rule updates and regional variations\n\nDashboard:\n- Visual dashboard for regulators showing:\n • Real-time compliance score\n • Trends by category, brand, or seller\n • Exportable violation reports\n • Geo-tagged compliance heatmaps (if applicable)\n\nScalability & Security:\n- Cloud-based architecture for large-scale crawling\n- Secure data logging and access control for regulators\n\nTarget Participants:\n- Final year UG (B.Tech/B.E.) students in Electronics, Embedded Systems, Instrumentation, AI/ML, Cybersecurity\n- PG/Ph.D. researchers in IoT Security, Metrology, Data Forensics\n- Startups or academia-industry teams working on compliance technologies\n\nEvaluation Criteria:\n- Innovation (Use of novel AI/ML/NLP or scraping techniques)\n- Accuracy (Precision in identifying compliant and non-compliant listings)\n- Scalability (Performance on large product databases)\n- User Experience (Ease of dashboard, actionable reports)\n- Alignment with Legal Metrology Rules\n- Regulatory Applicability (Can be deployed by Govt. departments with minimal changes)\n\nExpected Deliverables:\n(a) A working prototype of the compliance checker tool (Web-based or standalone app)\n(b) Technical documentation including:\n • Data pipeline design\n • OCR and ML models used\n • Validation methodology\n(c) Sample dataset with annotation (compliant/non-compliant examples)\n(d) Dashboard demo with live/test crawl outputs\n(e) Feasibility report for deployment with government bodies\n\nBenefits and Impact:\n- Saves time and effort in manual inspection of product declarations\n- Promotes fair trade and protects consumers from deceptive practices\n- Enables data-driven enforcement of Legal Metrology Act\n- Prepares India for next-gen regulation of digital marketplaces\n\nMentorship and Resources:\n- Legal Metrology Act and Rules/guidelines/advisories\n- Access to curated datasets of product images and declarations\n- Webinars by domain experts from the Ministry/Legal Metrology Division\n- Access to sandbox e-commerce API for testing solutions\n\nFuture Prospects:\n- Integration with a national regulatory enforcement portal\n- Use as a plug-in for e-commerce portals for real-time seller feedback\n- The scheme may be evaluated by the officers IILM, Ranchi, RRSLs and LM Hq, Delhi",
"ps_number": "SIH25057",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Automated Compliance Checker for Legal Metrology Declarations on E-Commerce Platforms"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "Weighing and measuring instruments are critical components in trade, industry, health, agriculture, and public safety. They ensure fair transactions and consumer trust when used accurately. However, tampering with such instruments, especially digital weighing scales, fuel dispensers, water/electricity meters, and retail POS systems-leads to fraudulent trade practices, losses to consumers, and damage to the integrity of commerce. Tampering may involve unauthorized hardware modifications, altered firmware, embedded malware, hidden switches, or even remote-control manipulation. These alterations are designed to evade detection by regulators while enabling cheating during operation.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": "-Innovation (new methods of detection or prevention)\n-Accuracy of tamper detection (false positive/negative rates)\n-Feasibility and cost of integrating the solution into common instruments\n-User Interface & Regulatory Dashboard usability\n-Readiness for field deployment and validation\n\nExpected Deliverables\n\n(a) Prototype device or software module to detect tampering (hardware and/or firmware)\n(b) Dashboard or interface for regulators to view alerts and device health\n(c) Tamper-logging and reporting features\n(d) Validation data from controlled tamper scenarios\n(e) Whitepaper/ technical documentation describing architecture, logic, limitations and future potential\n\nBenefits and Impact\n\n-Strengthens consumer protection and fairness in trade\n-Builds a proactive regulatory ecosystem using smart devices\n-Reduces inspection burden via automated alerting\n-Enables certification of tamper-proof weighing/measuring instruments\n-Contributes to digital trust in India's commerce and metrology infrastructure\n\nMentorship and Resources\n\n-Mentorship from Legal Metrology and cybersecuirty experts\n-Access to LM Act & Rules/ OIML Recommendations, guidelines, testing labs, etc.\n\nFuture Prospects\n\n-Development of National Digital Registry for compliant instruments\n-Integration with e-maap portal for real-time monitoring\n-Standardization of tamper-detection modules in certified devices\n-Development of tamper-proof metrology solutions",
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
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"innovative_features": null,
"introduction": null,
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"key_features": null,
"key_performance_parameters": null,
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"problem_description": "Develop a robust and intelligent system to detect, prevent, and alert against tampering in weighing and measuring instruments. The system must be capable of identifying both physical and digital tampering attempts in real time or near-real time. It should ideally include an embedded solution with secure logging, alert triggers, and remote verification capability by Legal Metrology officers. The proposed solution may also leverage Al/ML, digital forensics, tamper-evident Hardware seals, blockchain-backed audit trails, or sensor-based integrity checks.\n\nKey Functional Requirements\n\nTamper Detection:\n-Detect unauthorized access to device internals (e.g., seals broken, circuit modifications, etc.)\n-Monitor anomalies in calibration, weight drift, or suspicious patterns\n-Firmware Integrity\n-Secure boot or hash verification for firmware changes\n-Logging of unauthorized software firmware updates\n\nAlert System:\n-Instant tamper alerts to remote Legal Metrology dashboards\n-Optional local visual/audio alarms on tamper detection\n\nData Logging & Traceability:\n-Immutable logs of measurements, maintenance, and access events\n-Optionally backed by blockchain or secure cloud\n\nRemote Monitoring:\n-IoT integration to transmit logs to a centralized monitoring system\n-GPS/ geotagging to detect movement of stationary devices\n\nTarget Participants\n\nFinal year UG (B.Tech/B.E) students in Electronics, Embedded Systems, Instrumentation, AI/ML. Cybersecurity\n-PG/Ph.D. researchers in IoT Security, Metrology, Data Forensics\n-Startups or academia-industry teams working on compliance technologies",
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Consumer Affairs Food & Public Distribution (MoCA F&PD)",
"problem_description": "Background\n\nWeighing and measuring instruments are critical components in trade, industry, health, agriculture, and public safety. They ensure fair transactions and consumer trust when used accurately. However, tampering with such instruments, especially digital weighing scales, fuel dispensers, water/electricity meters, and retail POS systems-leads to fraudulent trade practices, losses to consumers, and damage to the integrity of commerce. Tampering may involve unauthorized hardware modifications, altered firmware, embedded malware, hidden switches, or even remote-control manipulation. These alterations are designed to evade detection by regulators while enabling cheating during operation.\n\nProblem Description\n\nDevelop a robust and intelligent system to detect, prevent, and alert against tampering in weighing and measuring instruments. The system must be capable of identifying both physical and digital tampering attempts in real time or near-real time. It should ideally include an embedded solution with secure logging, alert triggers, and remote verification capability by Legal Metrology officers. The proposed solution may also leverage Al/ML, digital forensics, tamper-evident Hardware seals, blockchain-backed audit trails, or sensor-based integrity checks.\n\nKey Functional Requirements\n\nTamper Detection:\n-Detect unauthorized access to device internals (e.g., seals broken, circuit modifications, etc.)\n-Monitor anomalies in calibration, weight drift, or suspicious patterns\n-Firmware Integrity\n-Secure boot or hash verification for firmware changes\n-Logging of unauthorized software firmware updates\n\nAlert System:\n-Instant tamper alerts to remote Legal Metrology dashboards\n-Optional local visual/audio alarms on tamper detection\n\nData Logging & Traceability:\n-Immutable logs of measurements, maintenance, and access events\n-Optionally backed by blockchain or secure cloud\n\nRemote Monitoring:\n-IoT integration to transmit logs to a centralized monitoring system\n-GPS/ geotagging to detect movement of stationary devices\n\nTarget Participants\n\nFinal year UG (B.Tech/B.E) students in Electronics, Embedded Systems, Instrumentation, AI/ML. Cybersecurity\n-PG/Ph.D. researchers in IoT Security, Metrology, Data Forensics\n-Startups or academia-industry teams working on compliance technologies\n\nEvaluation Criteria\n\n-Innovation (new methods of detection or prevention)\n-Accuracy of tamper detection (false positive/negative rates)\n-Feasibility and cost of integrating the solution into common instruments\n-User Interface & Regulatory Dashboard usability\n-Readiness for field deployment and validation\n\nExpected Deliverables\n\n(a) Prototype device or software module to detect tampering (hardware and/or firmware)\n(b) Dashboard or interface for regulators to view alerts and device health\n(c) Tamper-logging and reporting features\n(d) Validation data from controlled tamper scenarios\n(e) Whitepaper/ technical documentation describing architecture, logic, limitations and future potential\n\nBenefits and Impact\n\n-Strengthens consumer protection and fairness in trade\n-Builds a proactive regulatory ecosystem using smart devices\n-Reduces inspection burden via automated alerting\n-Enables certification of tamper-proof weighing/measuring instruments\n-Contributes to digital trust in India's commerce and metrology infrastructure\n\nMentorship and Resources\n\n-Mentorship from Legal Metrology and cybersecuirty experts\n-Access to LM Act & Rules/ OIML Recommendations, guidelines, testing labs, etc.\n\nFuture Prospects\n\n-Development of National Digital Registry for compliant instruments\n-Integration with e-maap portal for real-time monitoring\n-Standardization of tamper-detection modules in certified devices\n-Development of tamper-proof metrology solutions",
"ps_number": "SIH25058",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Detection and Prevention of Tampering in Weighing and Measuring instruments"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "SCD-V Division, DoSJE implements Centrally sponsored schemes 'Pre-Matric scholarships schemes for SCs & Others' and 'Post-Matric scholarships schemes for SC students'. The entire scholarship amount - both from the State and Central Government is paid directly into the account of the students ONLY through Direct Beneficiary Transfer (DBT) enabled Aadhaar seeded bank account. However, many students are unaware of the distinction between Aadhaar linked and Direct Beneficiary Transfer (DBT) enabled Aadhaar seeded bank account. As a result, a significant number of students do not have Aadhaar-seeded bank account, leading to delays in scholarships disbursement and posing challenges for them.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "The above problem statement envisages that many students are unaware of the distinction between Aadhaar linked and Direct Beneficiary Transfer (DBT) enabled Aadhaar seeded bank account. As a result, a significant number of students do not have Aadhaar-seeded bank account, leading to delays in scholarships disbursement and posing challenges for them. Government of India is continuously providing support to promote digital inclusion, however there is need of extensive awareness on the importance and procedures of Aadhaar seeding with Bank Accounts.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "The students need to be made more aware about the difference between Aadhaar linked and Direct Beneficiary Transfer (DBT) enabled Aadhaar seeded bank account for availing pre & post-Matric scholarships scheme for SCs. Extensive awareness programmes should be conducted through Gram Panchayats Notice Boards, school committees, and discussions in the parent-teacher association meetings and other public awareness measures.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
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},
"organization": "Ministry of Social Justice & Empowerment (MoSJE)",
"problem_description": "Background\n\nSCD-V Division, DoSJE implements Centrally sponsored schemes 'Pre-Matric scholarships schemes for SCs & Others' and 'Post-Matric scholarships schemes for SC students'. The entire scholarship amount - both from the State and Central Government is paid directly into the account of the students ONLY through Direct Beneficiary Transfer (DBT) enabled Aadhaar seeded bank account. However, many students are unaware of the distinction between Aadhaar linked and Direct Beneficiary Transfer (DBT) enabled Aadhaar seeded bank account. As a result, a significant number of students do not have Aadhaar-seeded bank account, leading to delays in scholarships disbursement and posing challenges for them.\n\nDescription\n\nThe above problem statement envisages that many students are unaware of the distinction between Aadhaar linked and Direct Beneficiary Transfer (DBT) enabled Aadhaar seeded bank account. As a result, a significant number of students do not have Aadhaar-seeded bank account, leading to delays in scholarships disbursement and posing challenges for them. Government of India is continuously providing support to promote digital inclusion, however there is need of extensive awareness on the importance and procedures of Aadhaar seeding with Bank Accounts.\n\nExpected Solution\n\nThe students need to be made more aware about the difference between Aadhaar linked and Direct Beneficiary Transfer (DBT) enabled Aadhaar seeded bank account for availing pre & post-Matric scholarships scheme for SCs. Extensive awareness programmes should be conducted through Gram Panchayats Notice Boards, school committees, and discussions in the parent-teacher association meetings and other public awareness measures.",
"ps_number": "SIH25059",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Enhancing Student Awareness on difference between Aadhaar linked and Direct Beneficiary Transfer(DBT) enabled Aadhaar seeded bank account"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "In FY 2021–22, India generated approximately 1.7 lakh tonnes of municipal solid waste daily, of which only 54% was scientifically treated. Around 24% was dumped in landfills and 22% remained unaccounted due to leakages in the waste management chain. This unaccounted waste is often burned or disposed of in open areas, drains, or water bodies, posing severe environmental and public health risks. (CEEW, 2025) This calls for strict solutions to enable all the waste generators to contribute to Waste Management.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "Overview: According to the CPCB’s Municipal Solid Waste (MSW) Annual Report for 2021-22, India generates approximately 1,70,339 tonnes of waste per day (TPD). Of this, around 1,56,449 TPD is collected. Out of the collected waste, 91,511 TPD is treated, while 41,455 TPD is disposed of in landfills. This leaves a significant gap of 37,373 TPD in effective waste management.\nWaste treatment: The total waste processed/treated across the country is 91,511 TPD. When compared to the total waste generated, which is 1,70,339 TPD, it is found that an average of 54% of the waste is treated/processed across the country.\nSolid Waste Processing Facilities: A total of 249 Waste-to-Energy plants, 819 Biomass Power plants and 50.8 lakhs small Biogas plants have been set up in the country to generate Power/Biogas/BioCNG.\n\nExpected Solutions\n\nThe proposals aim to develop a strict system to ensure that every citizen is trained on waste management, waste workers get strict training and required safety gear, waste management facilities are present in every ULB/GP, and a dedicated decentralised monitoring system is in place. The solutions are as follows:\n1. Mandatory training for every citizen: Similar to countries where military training is mandatory for every citizen, in India, there shall be mandatory Waste Management training for every Waste Generator/Citizen. This will ensure source segregation, which will result in better waste management, lesser load on waste management facilities and landfills.\n i. Physical training on types of waste, source segregation, how to make compost at home, re-use of plastic, etc.\n ii. Distribution of 3 types of dustbins for dry, wet and domestic hazardous waste to every household/citizen.\n iii. Distribution of a designed and tested compost making kit to every household/citizen.\n iv. App-based monitoring.\n2. Phase-wise training to all Waste Workers\n3. Formation of Area Committee for Monitoring – to be called as “Green Champions”: Decentralised approach for Monitoring has to be done at every step of Waste Management for bulk waste generators, residential areas, commercial & Public semi-public building, Industrial buildings, etc.\n i. Training of all Waste Generators\n ii. Waste Generation: Source segregation happening or not, no wet waste to be collected\n iii. Waste Collection happening on ground level\n iv. Waste transportation\n v. Waste treatment\n vi. Waste Disposal\n4. Incentive based approach: Incentive to Bulk waste generators and other buildings for source segregation.\n5. Waste Movements in ULBs: Taking example from Karnataka’s Yadgir city, “If you see waste, send photo” similar movement to be made mandatory for every ULB regardless of its size and population, to ensure community participation.\n6. Community Participation: One day of all working staff regardless of employment type to be engaged in Cleaning of Public areas, waste management. Inspired from Cleaning-day followed in schools every week, every waste generator to be responsible for keeping the country clean and contributing to waste management, starting from Govt. sector.\n7. Penalization system: Fines to be imposed and no waste collection for buildings not segregating waste at source as punishment.\n8. Waste Management Facilities to be made available in every ULB:\n i. Biomethanization plant\n ii. W-to-E plant\n iii. Multiple Recycling centres for different types of waste\n iv. Scrap collection shops – Online app based\n9. Complete Digital App-based system for:\n i. Training of all Waste Generators\n ii. Shopping of Waste utilities – compost kits, dustbins, etc.\n iii. Tracking of Waste collection vehicles\n iv. Location of Waste management facilities, Recycling centres, scrap shops etc.\n v. Option to upload geo-tagged photos of dumping sites, etc.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Social Justice & Empowerment (MoSJE)",
"problem_description": "Background\n\nIn FY 2021–22, India generated approximately 1.7 lakh tonnes of municipal solid waste daily, of which only 54% was scientifically treated. Around 24% was dumped in landfills and 22% remained unaccounted due to leakages in the waste management chain. This unaccounted waste is often burned or disposed of in open areas, drains, or water bodies, posing severe environmental and public health risks. (CEEW, 2025) This calls for strict solutions to enable all the waste generators to contribute to Waste Management.\n\nDescription\n\nOverview: According to the CPCB’s Municipal Solid Waste (MSW) Annual Report for 2021-22, India generates approximately 1,70,339 tonnes of waste per day (TPD). Of this, around 1,56,449 TPD is collected. Out of the collected waste, 91,511 TPD is treated, while 41,455 TPD is disposed of in landfills. This leaves a significant gap of 37,373 TPD in effective waste management.\nWaste treatment: The total waste processed/treated across the country is 91,511 TPD. When compared to the total waste generated, which is 1,70,339 TPD, it is found that an average of 54% of the waste is treated/processed across the country.\nSolid Waste Processing Facilities: A total of 249 Waste-to-Energy plants, 819 Biomass Power plants and 50.8 lakhs small Biogas plants have been set up in the country to generate Power/Biogas/BioCNG.\n\nExpected Solutions\n\nThe proposals aim to develop a strict system to ensure that every citizen is trained on waste management, waste workers get strict training and required safety gear, waste management facilities are present in every ULB/GP, and a dedicated decentralised monitoring system is in place. The solutions are as follows:\n1. Mandatory training for every citizen: Similar to countries where military training is mandatory for every citizen, in India, there shall be mandatory Waste Management training for every Waste Generator/Citizen. This will ensure source segregation, which will result in better waste management, lesser load on waste management facilities and landfills.\n i. Physical training on types of waste, source segregation, how to make compost at home, re-use of plastic, etc.\n ii. Distribution of 3 types of dustbins for dry, wet and domestic hazardous waste to every household/citizen.\n iii. Distribution of a designed and tested compost making kit to every household/citizen.\n iv. App-based monitoring.\n2. Phase-wise training to all Waste Workers\n3. Formation of Area Committee for Monitoring – to be called as “Green Champions”: Decentralised approach for Monitoring has to be done at every step of Waste Management for bulk waste generators, residential areas, commercial & Public semi-public building, Industrial buildings, etc.\n i. Training of all Waste Generators\n ii. Waste Generation: Source segregation happening or not, no wet waste to be collected\n iii. Waste Collection happening on ground level\n iv. Waste transportation\n v. Waste treatment\n vi. Waste Disposal\n4. Incentive based approach: Incentive to Bulk waste generators and other buildings for source segregation.\n5. Waste Movements in ULBs: Taking example from Karnataka’s Yadgir city, “If you see waste, send photo” similar movement to be made mandatory for every ULB regardless of its size and population, to ensure community participation.\n6. Community Participation: One day of all working staff regardless of employment type to be engaged in Cleaning of Public areas, waste management. Inspired from Cleaning-day followed in schools every week, every waste generator to be responsible for keeping the country clean and contributing to waste management, starting from Govt. sector.\n7. Penalization system: Fines to be imposed and no waste collection for buildings not segregating waste at source as punishment.\n8. Waste Management Facilities to be made available in every ULB:\n i. Biomethanization plant\n ii. W-to-E plant\n iii. Multiple Recycling centres for different types of waste\n iv. Scrap collection shops – Online app based\n9. Complete Digital App-based system for:\n i. Training of all Waste Generators\n ii. Shopping of Waste utilities – compost kits, dustbins, etc.\n iii. Tracking of Waste collection vehicles\n iv. Location of Waste management facilities, Recycling centres, scrap shops etc.\n v. Option to upload geo-tagged photos of dumping sites, etc.",
"ps_number": "SIH25060",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Clean & Green Technology",
"title": "Real life solutions for Waste Management"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "The agricultural region of Jorethang in South Sikkim experiences harsh, rainless summers and frequent water scarcity, making traditional irrigation unreliable and inefficient. Farmers struggle to provide adequate water to crops, often leading to reduced yields and wasted resources. With climate change intensifying these issues, there's a pressing need for a sustainable, smart irrigation approach that maximizes water efficiency and crop productivity. Integrating rainwater harvesting, sensor-based monitoring, and crop-specific intelligence offers a forward-thinking solution to these regional challenges.\n\nProposed Solution\n\nAgroSmart: A Sensor-Based Smart Irrigation System with Crop Intelligence and Rainwater Harvesting. This system aims to provide affordable, automated irrigation tailored to each crop’s needs using soil moisture and environmental sensors. It integrates a rainwater harvesting unit with real-time water level monitoring, ensuring efficient water use even during dry periods.\n\nThe solution will include:\n- Soil moisture and temperature sensors to monitor field conditions.\n- A crop database that determines optimal watering levels.\n- Automated valve control through microcontrollers (Arduino/ESP32).\n- Rainwater harvesting with level sensors to track available water.\n- A mobile/web dashboard for farmers to monitor data and control irrigation remotely.\n- Alerts and updates via SMS or app to keep farmers informed.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": "Conserves water, improves crop yields, minimizes manual labour, and helps farmers in Jorethang adapt to climate variability with a smart, self-sustaining irrigation system.",
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Sikkim",
"problem_description": "Background\n\nThe agricultural region of Jorethang in South Sikkim experiences harsh, rainless summers and frequent water scarcity, making traditional irrigation unreliable and inefficient. Farmers struggle to provide adequate water to crops, often leading to reduced yields and wasted resources. With climate change intensifying these issues, there's a pressing need for a sustainable, smart irrigation approach that maximizes water efficiency and crop productivity. Integrating rainwater harvesting, sensor-based monitoring, and crop-specific intelligence offers a forward-thinking solution to these regional challenges.\n\nProposed Solution\n\nAgroSmart: A Sensor-Based Smart Irrigation System with Crop Intelligence and Rainwater Harvesting. This system aims to provide affordable, automated irrigation tailored to each crop’s needs using soil moisture and environmental sensors. It integrates a rainwater harvesting unit with real-time water level monitoring, ensuring efficient water use even during dry periods.\n\nThe solution will include:\n- Soil moisture and temperature sensors to monitor field conditions.\n- A crop database that determines optimal watering levels.\n- Automated valve control through microcontrollers (Arduino/ESP32).\n- Rainwater harvesting with level sensors to track available water.\n- A mobile/web dashboard for farmers to monitor data and control irrigation remotely.\n- Alerts and updates via SMS or app to keep farmers informed.\n\nImpact\n\nConserves water, improves crop yields, minimizes manual labour, and helps farmers in Jorethang adapt to climate variability with a smart, self-sustaining irrigation system.",
"ps_number": "SIH25061",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Travel & Tourism",
"title": "Digitize and Showcase Monasteries of Sikkim for Tourism and Cultural Preservation"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "The agricultural region of Jorethang in South Sikkim experiences harsh, rainless summers and frequent water scarcity, making traditional irrigation unreliable and inefficient. Farmers struggle to provide adequate water to crops, often leading to reduced yields and wasted resources. With climate change intensifying these issues, there's a pressing need for a sustainable, smart irrigation approach that maximizes water efficiency and crop productivity. Integrating rainwater harvesting, sensor-based monitoring, and crop-specific intelligence offers a forward-thinking solution to these regional challenges.\n\nProposed Solution\n\nAgroSmart: A Sensor-Based Smart Irrigation System with Crop Intelligence and Rainwater Harvesting. This system aims to provide affordable, automated irrigation tailored to each crop’s needs using soil moisture and environmental sensors. It integrates a rainwater harvesting unit with real-time water level monitoring, ensuring efficient water use even during dry periods.\n\nThe solution will include:\n- Soil moisture and temperature sensors to monitor field conditions.\n- A crop database that determines optimal watering levels.\n- Automated valve control through microcontrollers (Arduino/ESP32).\n- Rainwater harvesting with level sensors to track available water.\n- A mobile/web dashboard for farmers to monitor data and control irrigation remotely.\n- Alerts and updates via SMS or app to keep farmers informed.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": "Conserves water, improves crop yields, minimizes manual labour, and helps farmers in Jorethang adapt to climate variability with a smart, self-sustaining irrigation system.",
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Sikkim",
"problem_description": "Background\n\nThe agricultural region of Jorethang in South Sikkim experiences harsh, rainless summers and frequent water scarcity, making traditional irrigation unreliable and inefficient. Farmers struggle to provide adequate water to crops, often leading to reduced yields and wasted resources. With climate change intensifying these issues, there's a pressing need for a sustainable, smart irrigation approach that maximizes water efficiency and crop productivity. Integrating rainwater harvesting, sensor-based monitoring, and crop-specific intelligence offers a forward-thinking solution to these regional challenges.\n\nProposed Solution\n\nAgroSmart: A Sensor-Based Smart Irrigation System with Crop Intelligence and Rainwater Harvesting. This system aims to provide affordable, automated irrigation tailored to each crop’s needs using soil moisture and environmental sensors. It integrates a rainwater harvesting unit with real-time water level monitoring, ensuring efficient water use even during dry periods.\n\nThe solution will include:\n- Soil moisture and temperature sensors to monitor field conditions.\n- A crop database that determines optimal watering levels.\n- Automated valve control through microcontrollers (Arduino/ESP32).\n- Rainwater harvesting with level sensors to track available water.\n- A mobile/web dashboard for farmers to monitor data and control irrigation remotely.\n- Alerts and updates via SMS or app to keep farmers informed.\n\nImpact\n\nConserves water, improves crop yields, minimizes manual labour, and helps farmers in Jorethang adapt to climate variability with a smart, self-sustaining irrigation system.",
"ps_number": "SIH25062",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "Implementation of Smart Agriculture for Efficient Cultivation in Hilly Regions"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "A cost-effective solution is required for detecting the breakage of Low Voltage AC Distribution Over Head conductors towards isolating the source of supply to prevent hazardous electric shock scenarios to the public, while also sending alarms to the concerned section office for immediate fault rectification.\n\nRecently, several cases of accidental deaths of children and adults due to electrocution from broken live conductors lying on the ground were reported in Kerala. Low voltage distribution lines often snap due to falling trees during natural calamities and also due to aging or corrosion. This dangerous condition is not detected or isolated by upstream fuses in these feeders because of high earth resistance and low fault current, leading to electrocution deaths when people come into contact with live bare conductors. Many existing solutions identified so far are not cost-effective or viable for the vast distribution network.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "An innovative solution based on IoT and AI, including edge intelligence, may provide an economical and scalable solution for this socially relevant problem faced by KSEBL.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Kerala",
"problem_description": "Background\n\nA cost-effective solution is required for detecting the breakage of Low Voltage AC Distribution Over Head conductors towards isolating the source of supply to prevent hazardous electric shock scenarios to the public, while also sending alarms to the concerned section office for immediate fault rectification.\n\nRecently, several cases of accidental deaths of children and adults due to electrocution from broken live conductors lying on the ground were reported in Kerala. Low voltage distribution lines often snap due to falling trees during natural calamities and also due to aging or corrosion. This dangerous condition is not detected or isolated by upstream fuses in these feeders because of high earth resistance and low fault current, leading to electrocution deaths when people come into contact with live bare conductors. Many existing solutions identified so far are not cost-effective or viable for the vast distribution network.\n\nExpected Solution\n\nAn innovative solution based on IoT and AI, including edge intelligence, may provide an economical and scalable solution for this socially relevant problem faced by KSEBL.",
"ps_number": "SIH25063",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Disaster Management",
"title": "Developing a cost effective solution for detecting the breakage of Low Voltage AC Distribution Over Head conductors"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "Kerala has a large penetration of Roof Top Solar PV systems amounting to nearly 1500 MW. These systems are connected to low voltage distribution feeders either in single-phase or three-phase mode. A majority of the systems are below 5 kW and hence get connected in single-phase, which causes severe unbalanced loading in feeders and results in an uneven voltage profile. This unbalanced loading also limits the quantum of PV that can be integrated into the network.\n\nAny load balancing work carried out during daytime under PV injection gets negated during the evening peak hours when solar power is unavailable. This situation calls for a solution that can dynamically reconfigure the network based on renewable energy (RE) injection and local demand scenarios.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "A cost-effective controller incorporating IoT and edge intelligence can be developed at the local node level for auto-balancing of the load across various phases in a feeder under the above conditions.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Kerala",
"problem_description": "Background\n\nKerala has a large penetration of Roof Top Solar PV systems amounting to nearly 1500 MW. These systems are connected to low voltage distribution feeders either in single-phase or three-phase mode. A majority of the systems are below 5 kW and hence get connected in single-phase, which causes severe unbalanced loading in feeders and results in an uneven voltage profile. This unbalanced loading also limits the quantum of PV that can be integrated into the network.\n\nAny load balancing work carried out during daytime under PV injection gets negated during the evening peak hours when solar power is unavailable. This situation calls for a solution that can dynamically reconfigure the network based on renewable energy (RE) injection and local demand scenarios.\n\nExpected Solution\n\nA cost-effective controller incorporating IoT and edge intelligence can be developed at the local node level for auto-balancing of the load across various phases in a feeder under the above conditions.",
"ps_number": "SIH25064",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Renewable / Sustainable Energy",
"title": "Improving the Renewable Energy hosting capacity in Distribution Feeders improving the Power quality of Distribution network during high RE injection"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Groundwater replenishment is a critical factor for the augmentation and sustainability of water resources in the country. There is significant potential in both rural and urban areas for harvesting rainwater from individual rooftops. The Central Ground Water Board (CGWB) has published several scientific manuals and reports on rooftop rainwater harvesting (RTRWH) potential, as well as FAQs and practical guides for artificial recharge. However, there is currently no user-friendly digital platform where individuals can directly assess their rainwater harvesting potential.\n\nProposed Solution\n\nTo promote public participation in groundwater conservation, it is proposed to develop a web/mobile application that enables users to easily estimate the feasibility of rooftop rainwater harvesting (RTRWH) and artificial recharge at their locations. By entering simple details such as name, location, number of dwellers, roof area, and available open space, the system will generate personalized outputs using GIS-based and algorithmic models.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": "The application will empower individuals and communities to take informed decisions about groundwater conservation and rainwater harvesting. It will enhance public awareness, encourage participation, and support sustainable water management efforts. The tool should also support regional languages for better accessibility and inclusivity.",
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": "- Feasibility check for rooftop rainwater harvesting\n- Suggested type of RTRWH/Artificial Recharge structures\n- Information on principal aquifer in the area\n- Depth to groundwater level\n- Local rainfall data\n- Runoff generation capacity\n- Recommended dimensions of recharge pits, trenches, and shafts\n- Cost estimation and cost-benefit analysis",
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Jal Shakti",
"problem_description": "Background\n\nGroundwater replenishment is a critical factor for the augmentation and sustainability of water resources in the country. There is significant potential in both rural and urban areas for harvesting rainwater from individual rooftops. The Central Ground Water Board (CGWB) has published several scientific manuals and reports on rooftop rainwater harvesting (RTRWH) potential, as well as FAQs and practical guides for artificial recharge. However, there is currently no user-friendly digital platform where individuals can directly assess their rainwater harvesting potential.\n\nProposed Solution\n\nTo promote public participation in groundwater conservation, it is proposed to develop a web/mobile application that enables users to easily estimate the feasibility of rooftop rainwater harvesting (RTRWH) and artificial recharge at their locations. By entering simple details such as name, location, number of dwellers, roof area, and available open space, the system will generate personalized outputs using GIS-based and algorithmic models.\n\nKey Features\n- Feasibility check for rooftop rainwater harvesting\n- Suggested type of RTRWH/Artificial Recharge structures\n- Information on principal aquifer in the area\n- Depth to groundwater level\n- Local rainfall data\n- Runoff generation capacity\n- Recommended dimensions of recharge pits, trenches, and shafts\n- Cost estimation and cost-benefit analysis\n\nImpact\n\nThe application will empower individuals and communities to take informed decisions about groundwater conservation and rainwater harvesting. It will enhance public awareness, encourage participation, and support sustainable water management efforts. The tool should also support regional languages for better accessibility and inclusivity.",
"ps_number": "SIH25065",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "Designing and development of an application for on spot assessment of Roof Top Rain water harvesting and artificial recharge potential and size of the RTRWH and AR"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "The Assessment of Dynamic Ground Water Resources of India is conducted annually by the Central Ground Water Board (CGWB) and State/UT Ground Water Departments, under the coordination of the Central Level Expert Group (CLEG), DoWR, RD & GR, MoJS. The assessment uses a GIS-based web application named INGRES (India Ground Water Resource Estimation System), developed by CGWB and IIT Hyderabad (https://ingres.iith.ac.in/home). The process estimates annual groundwater recharge, extractable resources, total extraction, and the stage of groundwater extraction for each assessment unit (Block/Mandal/Taluk). Units are categorized as Safe, Semi-Critical, Critical, or Over-Exploited, forming the scientific basis for groundwater management and regulation.\n\nCurrently, results are published via the INGRES portal, but users face challenges in retrieving results and historical data due to the vast database.\n\nProposed Solution\n\nTo improve accessibility, it is proposed to develop an AI-driven ChatBOT for INGRES. This intelligent virtual assistant will enable users to easily query groundwater data, access historical and current assessments, and obtain instant insights without navigating complex datasets.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": "The AI-powered ChatBOT will enhance user engagement, simplify access to groundwater resource data, and support informed decision-making for planners, researchers, policymakers, and the general public. It will make the INGRES portal more user-friendly, accessible, and effective in disseminating groundwater information.",
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": "- Intelligent query handling for groundwater estimation data\n- Real-time access to current and historical assessment results\n- Interactive scientific diagrams and visualizations\n- Multilingual support, including Indian regional languages\n- Seamless integration with the INGRES database for quick information retrieval",
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Jal Shakti",
"problem_description": "Background\n\nThe Assessment of Dynamic Ground Water Resources of India is conducted annually by the Central Ground Water Board (CGWB) and State/UT Ground Water Departments, under the coordination of the Central Level Expert Group (CLEG), DoWR, RD & GR, MoJS. The assessment uses a GIS-based web application named INGRES (India Ground Water Resource Estimation System), developed by CGWB and IIT Hyderabad (https://ingres.iith.ac.in/home). The process estimates annual groundwater recharge, extractable resources, total extraction, and the stage of groundwater extraction for each assessment unit (Block/Mandal/Taluk). Units are categorized as Safe, Semi-Critical, Critical, or Over-Exploited, forming the scientific basis for groundwater management and regulation.\n\nCurrently, results are published via the INGRES portal, but users face challenges in retrieving results and historical data due to the vast database.\n\nProposed Solution\n\nTo improve accessibility, it is proposed to develop an AI-driven ChatBOT for INGRES. This intelligent virtual assistant will enable users to easily query groundwater data, access historical and current assessments, and obtain instant insights without navigating complex datasets.\n\nKey Features\n- Intelligent query handling for groundwater estimation data\n- Real-time access to current and historical assessment results\n- Interactive scientific diagrams and visualizations\n- Multilingual support, including Indian regional languages\n- Seamless integration with the INGRES database for quick information retrieval\n\nImpact\n\nThe AI-powered ChatBOT will enhance user engagement, simplify access to groundwater resource data, and support informed decision-making for planners, researchers, policymakers, and the general public. It will make the INGRES portal more user-friendly, accessible, and effective in disseminating groundwater information.",
"ps_number": "SIH25066",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "Development of an AI-driven ChatBOT for INGRES as a virtual assistant"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "The presence of heavy metals in drinking water, even at trace levels, poses significant health risks, making accurate and timely assessment critical for public safety and environmental monitoring. Although several indices exist for assessing heavy metal pollution, their manual computation is tedious, time-consuming, inconsistent, and vulnerable to human error due to the complexity and variability of formulas.\n\nProposed Solution\n\nIt is proposed to develop an automated, user-friendly application that can compute Heavy Metal Pollution Indices (HMPI) in groundwater using standard formulas with minimal manual intervention. This tool will streamline calculations, reduce errors, and provide reliable outputs for stakeholders.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": "The application will provide accessible and reliable insights into groundwater heavy metal contamination, enabling better decision-making, enhanced environmental monitoring, and improved public health protection.",
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": "- Automated computation of heavy metal pollution indices using standard methodologies\n- Integration of groundwater heavy metal concentration datasets with geo-coordinates\n- Categorization of groundwater quality based on heavy metal presence\n- User-friendly interface for scientists, researchers, and policymakers\n- Reduction of manual effort and error-prone processes",
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Jal Shakti",
"problem_description": "Background\n\nThe presence of heavy metals in drinking water, even at trace levels, poses significant health risks, making accurate and timely assessment critical for public safety and environmental monitoring. Although several indices exist for assessing heavy metal pollution, their manual computation is tedious, time-consuming, inconsistent, and vulnerable to human error due to the complexity and variability of formulas.\n\nProposed Solution\n\nIt is proposed to develop an automated, user-friendly application that can compute Heavy Metal Pollution Indices (HMPI) in groundwater using standard formulas with minimal manual intervention. This tool will streamline calculations, reduce errors, and provide reliable outputs for stakeholders.\n\nKey Features\n- Automated computation of heavy metal pollution indices using standard methodologies\n- Integration of groundwater heavy metal concentration datasets with geo-coordinates\n- Categorization of groundwater quality based on heavy metal presence\n- User-friendly interface for scientists, researchers, and policymakers\n- Reduction of manual effort and error-prone processes\n\nImpact\n\nThe application will provide accessible and reliable insights into groundwater heavy metal contamination, enabling better decision-making, enhanced environmental monitoring, and improved public health protection.",
"ps_number": "SIH25067",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Proposal for Design and development of application for Heavy Metal Pollution indices"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "The presence of heavy metals in drinking water, even at trace levels, poses significant health risks, making accurate and timely assessment critical for public safety and environmental monitoring. Although several indices exist for assessing heavy metal pollution, their manual computation is tedious, time-consuming, inconsistent, and vulnerable to human error due to the complexity and variability of formulas.\n\nProposed Solution\n\nIt is proposed to develop an automated, user-friendly application that can compute Heavy Metal Pollution Indices (HMPI) in groundwater using standard formulas with minimal manual intervention. This tool will streamline calculations, reduce errors, and provide reliable outputs for stakeholders.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": "The application will provide accessible and reliable insights into groundwater heavy metal contamination, enabling better decision-making, enhanced environmental monitoring, and improved public health protection.",
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": "- Automated computation of heavy metal pollution indices using standard methodologies\n- Integration of groundwater heavy metal concentration datasets with geo-coordinates\n- Categorization of groundwater quality based on heavy metal presence\n- User-friendly interface for scientists, researchers, and policymakers\n- Reduction of manual effort and error-prone processes",
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Jal Shakti",
"problem_description": "Background\n\nThe presence of heavy metals in drinking water, even at trace levels, poses significant health risks, making accurate and timely assessment critical for public safety and environmental monitoring. Although several indices exist for assessing heavy metal pollution, their manual computation is tedious, time-consuming, inconsistent, and vulnerable to human error due to the complexity and variability of formulas.\n\nProposed Solution\n\nIt is proposed to develop an automated, user-friendly application that can compute Heavy Metal Pollution Indices (HMPI) in groundwater using standard formulas with minimal manual intervention. This tool will streamline calculations, reduce errors, and provide reliable outputs for stakeholders.\n\nKey Features\n- Automated computation of heavy metal pollution indices using standard methodologies\n- Integration of groundwater heavy metal concentration datasets with geo-coordinates\n- Categorization of groundwater quality based on heavy metal presence\n- User-friendly interface for scientists, researchers, and policymakers\n- Reduction of manual effort and error-prone processes\n\nImpact\n\nThe application will provide accessible and reliable insights into groundwater heavy metal contamination, enabling better decision-making, enhanced environmental monitoring, and improved public health protection.",
"ps_number": "SIH25068",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Real time Groundwater resource evaluation using DWLR data"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "As industries increasingly emphasize sustainability, Life Cycle Assessment (LCA) is emerging not just as a tool for measuring environmental impact, but as a key strategy for advancing circularity. Metals like aluminium and copper, vital to sectors from energy to infrastructure, undergo multiple stages — from extraction and processing to use and end-of-life. Traditional linear models often result in resource depletion and waste. Modern LCA frameworks now assess not only emissions and resource use, but also the potential for reuse, recycling, and closed-loop systems. By informing decisions on product design, manufacturing, and end-of-life recovery, LCA enables a shift toward a circular economy where materials are kept in use longer and waste is minimized.\n\nProblem Statement\n\nThere is a need to design an intuitive, AI-powered software platform that enables metallurgists, engineers, and decision-makers to perform automated LCAs for metals such as aluminium, copper, or critical minerals, with a special emphasis on circularity.\n\nProposed Solution\nThe platform should:\n- Allow users to input or select process and production details (including raw vs. recycled routes, energy use, transport, and end-of-life options).\n- Use AI/ML models to estimate missing parameters and predict both environmental and circularity indicators (e.g., recycled content, resource efficiency, extended product life, potential for reuse).\n- Visualize circular flow opportunities alongside environmental impacts across the full value chain — from raw material extraction through reuse or recycling.\n- Enable easy comparison of conventional and circular processing pathways.\n- Generate actionable reports and recommendations for reducing impacts and enhancing circularity, even when users have limited data or specialized expertise.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": "With this tool, the metals sector will be empowered to make practical, data-driven choices that foster environmental sustainability while advancing circular, resource-efficient systems.",
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Mines",
"problem_description": "Background\n\nAs industries increasingly emphasize sustainability, Life Cycle Assessment (LCA) is emerging not just as a tool for measuring environmental impact, but as a key strategy for advancing circularity. Metals like aluminium and copper, vital to sectors from energy to infrastructure, undergo multiple stages — from extraction and processing to use and end-of-life. Traditional linear models often result in resource depletion and waste. Modern LCA frameworks now assess not only emissions and resource use, but also the potential for reuse, recycling, and closed-loop systems. By informing decisions on product design, manufacturing, and end-of-life recovery, LCA enables a shift toward a circular economy where materials are kept in use longer and waste is minimized.\n\nProblem Statement\n\nThere is a need to design an intuitive, AI-powered software platform that enables metallurgists, engineers, and decision-makers to perform automated LCAs for metals such as aluminium, copper, or critical minerals, with a special emphasis on circularity.\n\nProposed Solution\nThe platform should:\n- Allow users to input or select process and production details (including raw vs. recycled routes, energy use, transport, and end-of-life options).\n- Use AI/ML models to estimate missing parameters and predict both environmental and circularity indicators (e.g., recycled content, resource efficiency, extended product life, potential for reuse).\n- Visualize circular flow opportunities alongside environmental impacts across the full value chain — from raw material extraction through reuse or recycling.\n- Enable easy comparison of conventional and circular processing pathways.\n- Generate actionable reports and recommendations for reducing impacts and enhancing circularity, even when users have limited data or specialized expertise.\n\nImpact\n\nWith this tool, the metals sector will be empowered to make practical, data-driven choices that foster environmental sustainability while advancing circular, resource-efficient systems.",
"ps_number": "SIH25069",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "AI-Driven Life Cycle Assessment (LCA) Tool for Advancing Circularity and Sustainability in Metallurgy and Mining"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "India is facing a growing e-waste crisis, generating over 1.75 million tonnes annually. A key reason why millions of old laptops and smartphones remain unused or improperly discarded is fear of data breaches. Most users hesitate to recycle their devices due to concerns about sensitive personal or organizational data being recovered. Existing data sanitization tools are either too complex, expensive, or lack verifiable proof of erasure. This gap has led to over ₹50,000 crore worth of IT assets being hoarded in homes and offices, hindering circular economy efforts. A user-friendly, tamper-proof, and auditable data wiping solution is urgently needed to promote safe disposal and reuse of electronic devices.\n\nProblem Statement\n\nDesign and prototype a secure, cross-platform data wiping application that works on Windows, Linux, and Android devices. The tool must:\n- Securely erase all user data, including hidden storage areas like HPA/DCO and SSD sectors.\n- Generate a digitally signed, tamper-proof wipe certificate (in PDF and JSON formats).\n- Feature an intuitive one-click interface suitable for general public use.\n- Offer offline usability (e.g., bootable ISO/USB).\n- Enable third-party verification of wipe status.\n- Be scalable, standards-compliant (aligned with NIST SP 800-88), and support trust and transparency in IT asset disposal and recycling workflows.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": "This solution will build user confidence in device recycling, reduce hoarding of IT assets, promote safe e-waste management, and advance India’s circular economy initiatives.",
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Mines",
"problem_description": "Background\n\nIndia is facing a growing e-waste crisis, generating over 1.75 million tonnes annually. A key reason why millions of old laptops and smartphones remain unused or improperly discarded is fear of data breaches. Most users hesitate to recycle their devices due to concerns about sensitive personal or organizational data being recovered. Existing data sanitization tools are either too complex, expensive, or lack verifiable proof of erasure. This gap has led to over ₹50,000 crore worth of IT assets being hoarded in homes and offices, hindering circular economy efforts. A user-friendly, tamper-proof, and auditable data wiping solution is urgently needed to promote safe disposal and reuse of electronic devices.\n\nProblem Statement\n\nDesign and prototype a secure, cross-platform data wiping application that works on Windows, Linux, and Android devices. The tool must:\n- Securely erase all user data, including hidden storage areas like HPA/DCO and SSD sectors.\n- Generate a digitally signed, tamper-proof wipe certificate (in PDF and JSON formats).\n- Feature an intuitive one-click interface suitable for general public use.\n- Offer offline usability (e.g., bootable ISO/USB).\n- Enable third-party verification of wipe status.\n- Be scalable, standards-compliant (aligned with NIST SP 800-88), and support trust and transparency in IT asset disposal and recycling workflows.\n\nImpact\n\nThis solution will build user confidence in device recycling, reduce hoarding of IT assets, promote safe e-waste management, and advance India’s circular economy initiatives.",
"ps_number": "SIH25070",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Secure Data Wiping for Trustworthy IT Asset Recycling"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Rockfalls in open-pit mines pose a significant threat to both personnel and equipment, often resulting in serious injuries, operational delays, and financial loss. Traditional rockfall detection systems rely on visual inspection or expensive proprietary solutions, which are either labor-intensive or lack real-time predictive capabilities. With the increasing use of digital monitoring and AI, there is a pressing need to integrate predictive analytics into slope stability assessments for proactive decision-making.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "This problem invites participants to design a smart, AI-based system capable of predicting potential rockfall incidents in open-pit mines. The system should process multi-source data inputs including:\n- Digital Elevation Models (DEM)\n- Drone-captured imagery\n- Geotechnical sensor data (displacement, strain, pore pressure)\n- Environmental factors (rainfall, temperature, vibrations)\n\nMachine learning models should identify patterns that precede rockfall events. The system must feature a user-friendly dashboard for mine planners, along with:\n- Real-time risk maps\n- Probability-based forecasts\n- Alert mechanisms (via SMS/email)\nIntegration with low-cost monitoring hardware would be an added advantage.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "- A machine learning model trained on real or synthetic data to predict rockfall risks.\n- A web/mobile-based dashboard for visualization of vulnerable zones.\n- Real-time alert generation with suggested action plans.\n- Open-source integration possibilities for scale and customization.",
"impact": "The solution should be cost-effective, scalable for different mine sites, and adaptable for both public and private mining operations, enabling safer and more resilient mining practices.",
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Mines",
"problem_description": "Background\n\nRockfalls in open-pit mines pose a significant threat to both personnel and equipment, often resulting in serious injuries, operational delays, and financial loss. Traditional rockfall detection systems rely on visual inspection or expensive proprietary solutions, which are either labor-intensive or lack real-time predictive capabilities. With the increasing use of digital monitoring and AI, there is a pressing need to integrate predictive analytics into slope stability assessments for proactive decision-making.\n\nDescription\n\nThis problem invites participants to design a smart, AI-based system capable of predicting potential rockfall incidents in open-pit mines. The system should process multi-source data inputs including:\n- Digital Elevation Models (DEM)\n- Drone-captured imagery\n- Geotechnical sensor data (displacement, strain, pore pressure)\n- Environmental factors (rainfall, temperature, vibrations)\n\nMachine learning models should identify patterns that precede rockfall events. The system must feature a user-friendly dashboard for mine planners, along with:\n- Real-time risk maps\n- Probability-based forecasts\n- Alert mechanisms (via SMS/email)\nIntegration with low-cost monitoring hardware would be an added advantage.\n\nExpected Solution\n\n- A machine learning model trained on real or synthetic data to predict rockfall risks.\n- A web/mobile-based dashboard for visualization of vulnerable zones.\n- Real-time alert generation with suggested action plans.\n- Open-source integration possibilities for scale and customization.\n\nImpact\n\nThe solution should be cost-effective, scalable for different mine sites, and adaptable for both public and private mining operations, enabling safer and more resilient mining practices.",
"ps_number": "SIH25071",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Disaster Management",
"title": "AI-Based Rockfall Prediction and Alert System for Open-Pit Mines"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": "The current dewatering system at our copper mining site relies heavily on electrically operated pumps, often supplemented by diesel generators during power outages. This traditional setup not only leads to high operational expenses due to electricity and fuel consumption, but also contributes to increased carbon emissions, operational complexity, and maintenance challenges.\n\nIn light of rising energy costs and the need for reliable and sustainable mine dewatering solutions, there is a pressing need to explore alternative approaches. Solar-powered dewatering systems offer a viable solution, leveraging renewable energy to reduce costs, ensure consistent pump operation, and support broader environmental and sustainability goals. Implementing such systems under an OPEX (Operational Expenditure) model aligns with the strategy to minimize capital expenditure while transitioning to cleaner and more efficient mining practices.\n\nProblem\n\nThis outlines the implementation of solar-powered dewatering pumps at the copper mining site under an OPEX model. The system will integrate solar photovoltaic (PV) modules with energy-efficient submersible or surface pumps, designed to meet site-specific dewatering requirements.\n\nA third-party vendor will be engaged to install, operate, and maintain the system through a Power Purchase Agreement (PPA) or operational lease, eliminating the need for upfront capital investment by HCL. The system can be configured as stand-alone solar or hybrid (solar with grid/diesel backup) to ensure uninterrupted pumping. The proposed solution aims to reduce operational energy costs, enhance dewatering reliability, and support environmental commitments by minimizing diesel use and lowering carbon emissions.\n\nTechnical specifications are attached.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "Deployment of solar-powered, energy-efficient dewatering pumps under an OPEX model to ensure reliable, low-cost, and eco-friendly mine dewatering. The system will reduce diesel and electricity usage, include hybrid backup, and feature vendor-managed operation and maintenance.",
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Mines",
"problem_description": "Background\n\nThe current dewatering system at our copper mining site relies heavily on electrically operated pumps, often supplemented by diesel generators during power outages. This traditional setup not only leads to high operational expenses due to electricity and fuel consumption, but also contributes to increased carbon emissions, operational complexity, and maintenance challenges.\n\nIn light of rising energy costs and the need for reliable and sustainable mine dewatering solutions, there is a pressing need to explore alternative approaches. Solar-powered dewatering systems offer a viable solution, leveraging renewable energy to reduce costs, ensure consistent pump operation, and support broader environmental and sustainability goals. Implementing such systems under an OPEX (Operational Expenditure) model aligns with the strategy to minimize capital expenditure while transitioning to cleaner and more efficient mining practices.\n\nProblem\n\nThis outlines the implementation of solar-powered dewatering pumps at the copper mining site under an OPEX model. The system will integrate solar photovoltaic (PV) modules with energy-efficient submersible or surface pumps, designed to meet site-specific dewatering requirements.\n\nA third-party vendor will be engaged to install, operate, and maintain the system through a Power Purchase Agreement (PPA) or operational lease, eliminating the need for upfront capital investment by HCL. The system can be configured as stand-alone solar or hybrid (solar with grid/diesel backup) to ensure uninterrupted pumping. The proposed solution aims to reduce operational energy costs, enhance dewatering reliability, and support environmental commitments by minimizing diesel use and lowering carbon emissions.\n\nTechnical specifications are attached.\n\nExpected Solution\n\nDeployment of solar-powered, energy-efficient dewatering pumps under an OPEX model to ensure reliable, low-cost, and eco-friendly mine dewatering. The system will reduce diesel and electricity usage, include hybrid backup, and feature vendor-managed operation and maintenance.",
"ps_number": "SIH25072",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Renewable / Sustainable Energy",
"title": "Design and Implementation of Solar-Powered Dewatering in Mining Operations"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "Identifying and assessing athletic talent in a country as vast and diverse as India is a significant challenge. Aspiring athletes, particularly from rural and remote areas, often lack access to standardized assessment facilities or opportunities to showcase their talent. The absence of reliable and scalable talent assessment models hinders the discovery of potential athletes who could benefit from Government support. A set of standard fitness assessment tests - including height, weight, vertical jump, shuttle run, sit-ups, and endurance runs (Annexure A) - provides a scientific method to evaluate talent. However, the reach and implementation of such tests remain limited due to infrastructure constraints.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": "1. AI-based Cheat Detection: Identify anomalies or manipulations (e.g., tampered videos or incorrect movements) to ensure fair assessments.\n2. Offline Video Analysis: Perform preliminary performance analysis directly on the device without requiring continuous internet connectivity.\n3. Performance Benchmarking: Compare athlete performance against age/gender-based benchmarks, providing instant feedback.\n4. Gamified User Interface: Use progress badges, leaderboards, and interactive visuals to engage athletes and encourage participation.\n5. Auto-Test Segmentation: Automatically detect and segment performance clips (e.g., counting reps in sit-ups or analyzing vertical jumps) to reduce manual effort.\n\nExpected Deliverables\n\n1. A mobile application (Android/iOS) that allows video recording and assessment of athletes performance across the test batteries.\n2. AI/ML modules for on-device video analysis, verification of test results, and cheat detection.\n3. A secure backend system to transmit data to the Sports Authority of India for further processing.\n4. A dashboard for officials to view and evaluate verified performance data.\n\nExpected Impact\n\n1. Democratization of sports talent assessment, reaching even remote areas.\n2. Low-cost, scalable solution enabling mass participation in talent identification initiatives.\n3. Improved efficiency and transparency in evaluating and discovering potential athletes.",
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": "The Sports Authority of India (SAI) requires an innovative, mobile-based solution to democratize sports talent assessment. The proposed platform should:\n\n1. Enable athletes to download an app and record videos of their performance in the prescribed fitness assessment tests.\n2. Use AI/ML-based on-device verification to analyze the recorded videos for accuracy and authenticity (e.g., detecting jump height, counting sit-ups, or measuring time/distance in runs).\n3. Securely submit verified data to SAI servers for further evaluation and athlete profiling.\n4. Be low-cost and lightweight, ensuring accessibility even on entry-level smartphones and low-bandwidth networks.",
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Ministry of Youth Affairs and Sports",
"problem_description": "Background\n\nIdentifying and assessing athletic talent in a country as vast and diverse as India is a significant challenge. Aspiring athletes, particularly from rural and remote areas, often lack access to standardized assessment facilities or opportunities to showcase their talent. The absence of reliable and scalable talent assessment models hinders the discovery of potential athletes who could benefit from Government support. A set of standard fitness assessment tests - including height, weight, vertical jump, shuttle run, sit-ups, and endurance runs (Annexure A) - provides a scientific method to evaluate talent. However, the reach and implementation of such tests remain limited due to infrastructure constraints.\n\nProblem Description\n\nThe Sports Authority of India (SAI) requires an innovative, mobile-based solution to democratize sports talent assessment. The proposed platform should:\n\n1. Enable athletes to download an app and record videos of their performance in the prescribed fitness assessment tests.\n2. Use AI/ML-based on-device verification to analyze the recorded videos for accuracy and authenticity (e.g., detecting jump height, counting sit-ups, or measuring time/distance in runs).\n3. Securely submit verified data to SAI servers for further evaluation and athlete profiling.\n4. Be low-cost and lightweight, ensuring accessibility even on entry-level smartphones and low-bandwidth networks.\n\nInnovative Features\n\n1. AI-based Cheat Detection: Identify anomalies or manipulations (e.g., tampered videos or incorrect movements) to ensure fair assessments.\n2. Offline Video Analysis: Perform preliminary performance analysis directly on the device without requiring continuous internet connectivity.\n3. Performance Benchmarking: Compare athlete performance against age/gender-based benchmarks, providing instant feedback.\n4. Gamified User Interface: Use progress badges, leaderboards, and interactive visuals to engage athletes and encourage participation.\n5. Auto-Test Segmentation: Automatically detect and segment performance clips (e.g., counting reps in sit-ups or analyzing vertical jumps) to reduce manual effort.\n\nExpected Deliverables\n\n1. A mobile application (Android/iOS) that allows video recording and assessment of athletes performance across the test batteries.\n2. AI/ML modules for on-device video analysis, verification of test results, and cheat detection.\n3. A secure backend system to transmit data to the Sports Authority of India for further processing.\n4. A dashboard for officials to view and evaluate verified performance data.\n\nExpected Impact\n\n1. Democratization of sports talent assessment, reaching even remote areas.\n2. Low-cost, scalable solution enabling mass participation in talent identification initiatives.\n3. Improved efficiency and transparency in evaluating and discovering potential athletes.",
"ps_number": "SIH25073",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Fitness & Sports",
"title": "AI-Powered Mobile Platform for Democratizing Sports Talent Assessment"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
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"introduction": "Problem\n\nKerala’s smallholder farmers often lack access to personalized, timely agricultural advice. Generic advisories fail to account for local crop choices, weather, soil conditions, or farming practices. Many farmers also don’t maintain records of their activities, which limits learning from past seasons and accessing scheme benefits.\n\nChallenge\n\nBuild an AI-powered personal farming assistant that acts like a digital companion for each farmer—understanding their specific context, guiding their actions, and learning over time.\n\nCore Features\n\n1. Farmer & Farm Profiling: Capture key details—location, land size, crop, soil type, irrigation.\n2. Conversational Interface: Enable farmers to interact in Malayalam via voice or text.\n3. Activity Tracking: Let farmers log events like sowing, irrigation, input use, or pest issues in simple language.\n4. Personalized Advisory: Use AI to give proactive, contextual guidance—e.g., “Rain expected, avoid spraying tomorrow,” or “Pest outbreak reported nearby—inspect your brinjal crop.”\n5. Reminders & Alerts: Send timely nudges for crop operations, scheme deadlines, and price trends.\n6. Knowledge Engine: Pull from local crop calendars, pest data, and best practices to continuously improve recommendations.\n\nExpected Impact\n\n- Empowers farmers with personalized, on-demand support.\n- Enhances productivity and sustainability through timely actions.\n- Bridges the knowledge gap using AI + local context.\n\nThis solution aims to create a “Krishi Sakhi”—a digital friend who walks with the farmer throughout the crop cycle. The fund availability is subject to availability through government sanction.",
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},
"organization": "Government of Kerala",
"problem_description": "Problem\n\nKerala’s smallholder farmers often lack access to personalized, timely agricultural advice. Generic advisories fail to account for local crop choices, weather, soil conditions, or farming practices. Many farmers also don’t maintain records of their activities, which limits learning from past seasons and accessing scheme benefits.\n\nChallenge\n\nBuild an AI-powered personal farming assistant that acts like a digital companion for each farmer—understanding their specific context, guiding their actions, and learning over time.\n\nCore Features\n\n1. Farmer & Farm Profiling: Capture key details—location, land size, crop, soil type, irrigation.\n2. Conversational Interface: Enable farmers to interact in Malayalam via voice or text.\n3. Activity Tracking: Let farmers log events like sowing, irrigation, input use, or pest issues in simple language.\n4. Personalized Advisory: Use AI to give proactive, contextual guidance—e.g., “Rain expected, avoid spraying tomorrow,” or “Pest outbreak reported nearby—inspect your brinjal crop.”\n5. Reminders & Alerts: Send timely nudges for crop operations, scheme deadlines, and price trends.\n6. Knowledge Engine: Pull from local crop calendars, pest data, and best practices to continuously improve recommendations.\n\nExpected Impact\n\n- Empowers farmers with personalized, on-demand support.\n- Enhances productivity and sustainability through timely actions.\n- Bridges the knowledge gap using AI + local context.\n\nThis solution aims to create a “Krishi Sakhi”—a digital friend who walks with the farmer throughout the crop cycle. The fund availability is subject to availability through government sanction.",
"ps_number": "SIH25074",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "AI-Powered Personal Farming Assistant for Kerala Farmers"
}
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"introduction": "Problem\n\nDespite numerous awareness campaigns, many farmers still rely on unsustainable practices—excessive chemical use, over-irrigation, or mono-cropping—due to habit, lack of training, or limited engagement. Traditional training methods often fail to inspire lasting behavioural change, especially among younger farmers.\n\nChallenge\n\nDesign a gamified digital platform that educates and motivates farmers to adopt sustainable agricultural practices through interactive challenges, rewards, and community participation.\n\nCore Features\n\n- Learning Through Play: Convert best practices—such as organic input use, mixed cropping, soil health management—into engaging missions or tasks.\n- Personalized Quests: Tailor challenges based on the farmer’s crop, location, and farm size—e.g., “Complete 3 weeks of mulching on banana fields” or “Switch to bio-pesticides this season.”\n- Progress Tracker: Visual dashboards showing farmer progress, sustainability score, and learning badges.\n- Peer Sharing & Leaderboards: Allow farmers to share progress and earn recognition locally or panchayat-wise.\n- Incentive System: Link rewards to real-world benefits like scheme eligibility points, training credits, or public recognition.\n\nExpected Impact\n\n- Encourages adoption of eco-friendly practices in a fun, engaging way.\n- Builds a digitally connected community of progressive farmers.\n- Makes sustainable farming accessible, especially for youth and early adopters.\n\nThis platform transforms agricultural extension from top-down instruction to a bottom-up, gamified experience—making sustainable farming a goal, a game, and a movement.",
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},
"organization": "Government of Kerala",
"problem_description": "Problem\n\nDespite numerous awareness campaigns, many farmers still rely on unsustainable practices—excessive chemical use, over-irrigation, or mono-cropping—due to habit, lack of training, or limited engagement. Traditional training methods often fail to inspire lasting behavioural change, especially among younger farmers.\n\nChallenge\n\nDesign a gamified digital platform that educates and motivates farmers to adopt sustainable agricultural practices through interactive challenges, rewards, and community participation.\n\nCore Features\n\n- Learning Through Play: Convert best practices—such as organic input use, mixed cropping, soil health management—into engaging missions or tasks.\n- Personalized Quests: Tailor challenges based on the farmer’s crop, location, and farm size—e.g., “Complete 3 weeks of mulching on banana fields” or “Switch to bio-pesticides this season.”\n- Progress Tracker: Visual dashboards showing farmer progress, sustainability score, and learning badges.\n- Peer Sharing & Leaderboards: Allow farmers to share progress and earn recognition locally or panchayat-wise.\n- Incentive System: Link rewards to real-world benefits like scheme eligibility points, training credits, or public recognition.\n\nExpected Impact\n\n- Encourages adoption of eco-friendly practices in a fun, engaging way.\n- Builds a digitally connected community of progressive farmers.\n- Makes sustainable farming accessible, especially for youth and early adopters.\n\nThis platform transforms agricultural extension from top-down instruction to a bottom-up, gamified experience—making sustainable farming a goal, a game, and a movement.",
"ps_number": "SIH25075",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "Gamified Platform to Promote Sustainable Farming Practices"
}
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"introduction": "Problem\n\nFarmers often face critical questions related to pests, weather, inputs, subsidies, and market trends—but timely expert advice is rarely accessible. Agri officers and helplines are overburdened, and existing services don’t scale to support individual needs across diverse regions, languages, and literacy levels.\n\nChallenge\n\nBuild an AI-powered advisory system that allows farmers to ask queries in their own language (preferably Malayalam) and receive accurate, context-aware answers instantly.\n\nCore Features\n\n- Natural Language Query Handling: Farmers can ask questions via voice or text in Malayalam—e.g., “Which pesticide for leaf spot in my banana?”\n- Multimodal Inputs: Support for image uploads (e.g., diseased crop photo) or voice notes.\n- AI-Powered Knowledge Engine: Use LLMs and fine-tuned agri datasets to provide reliable, tailored answers—drawing from local crop calendars, pest advisories, weather, and scheme guidelines.\n- Context Awareness: Factor in the farmer’s location, crop, season, and history (if known) to give personalized advice.\n- Escalation System: For complex or unclear queries, escalate to local agri officers with context and suggestions.\n- Learning Loop: Continuously improve the system using real queries, feedback, and local expert inputs.\n\nExpected Impact\n\n- Makes expert-level farming advice instantly accessible to all.\n- Bridges the communication gap between farmers and extension systems.\n- Supports Krishibhavans and Agri Departments by automating first-level support.\n\nThis system aims to become a “Digital Krishi Officer”—always available, always learning, and always farmer-first.",
"iot_integration_optional": null,
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},
"organization": "Government of Kerala",
"problem_description": "Problem\n\nFarmers often face critical questions related to pests, weather, inputs, subsidies, and market trends—but timely expert advice is rarely accessible. Agri officers and helplines are overburdened, and existing services don’t scale to support individual needs across diverse regions, languages, and literacy levels.\n\nChallenge\n\nBuild an AI-powered advisory system that allows farmers to ask queries in their own language (preferably Malayalam) and receive accurate, context-aware answers instantly.\n\nCore Features\n\n- Natural Language Query Handling: Farmers can ask questions via voice or text in Malayalam—e.g., “Which pesticide for leaf spot in my banana?”\n- Multimodal Inputs: Support for image uploads (e.g., diseased crop photo) or voice notes.\n- AI-Powered Knowledge Engine: Use LLMs and fine-tuned agri datasets to provide reliable, tailored answers—drawing from local crop calendars, pest advisories, weather, and scheme guidelines.\n- Context Awareness: Factor in the farmer’s location, crop, season, and history (if known) to give personalized advice.\n- Escalation System: For complex or unclear queries, escalate to local agri officers with context and suggestions.\n- Learning Loop: Continuously improve the system using real queries, feedback, and local expert inputs.\n\nExpected Impact\n\n- Makes expert-level farming advice instantly accessible to all.\n- Bridges the communication gap between farmers and extension systems.\n- Supports Krishibhavans and Agri Departments by automating first-level support.\n\nThis system aims to become a “Digital Krishi Officer”—always available, always learning, and always farmer-first.",
"ps_number": "SIH25076",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Agriculture FoodTech & Rural Development",
"title": "AI-Based Farmer Query Support and Advisory System"
}
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{
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"introduction": "A hardware that can detect and prevent unauthorized use of electric fences",
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},
"organization": "Government of Kerala",
"problem_description": "A hardware that can detect and prevent unauthorized use of electric fences",
"ps_number": "SIH25077",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "A hardware that can detect and prevent unauthorized use of electric fences"
}
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{
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"introduction": "Software other than a circuit breaker that can be used to detect and turn off LT lines when the line breaks.",
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"organization": "Government of Kerala",
"problem_description": "Software other than a circuit breaker that can be used to detect and turn off LT lines when the line breaks.",
"ps_number": "SIH25079",
"s_no": 0,
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"theme": "Disaster Management",
"title": "Software other than a circuit breaker that can be used to detect and turn off LT lines when the line breaks"
}
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{
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"introduction": "Since its first commercial run in 2017, KMRL has grown into a complex, multidisciplinary enterprise that stretches far beyond train operations. Every business day the organization generates and receives thousands of pages of material: engineering drawings, maintenance job cards, incident reports, vendor invoices, purchase-order correspondence, regulatory directives, environmental-impact studies, safety circulars, HR policies, legal opinions, and board-meeting minutes. These arrive through e-mail, Maximo exports, SharePoint repositories, WhatsApp PDFs, hard-copy scans, and ad-hoc cloud links—often in both English and Malayalam, sometimes in bilingual hybrids, frequently with embedded tables, photos, or signatures. The sheer diversity and volume have created a silent productivity tax: ● Information latency: Front-line managers spend hours skimming lengthy documents for the few actionable lines that affect their shift, delaying decisions on train availability, contractor payments, or staffing reallocations. ● Siloed awareness: Procurement may negotiate a spare-parts contract without realizing that Engineering has already flagged an upcoming design change HR may schedule refresher training unaware of a new safety bulletin released the previous evening. ● Compliance exposure: Regulatory updates from the Commissioner of Metro Rail Safety and the Ministry of Housing & Urban Affairs are buried in inboxes, risking missed deadlines or audit non-conformities. ● Knowledge attrition: Institutional memory remains locked in static files when key personnel transfer or retire, hard-won insights vanish with them. ● Duplicated effort: Different teams independently create summaries or slide decks of the same source documents, multiplying manual work and version-control headaches. As KMRL prepares to expand its corridor, add two new depots, and integrate emerging technologies such as Unified Namespace (UNS) data streams and IoT condition monitoring, the documentary burden will only intensify. Without an organization-wide mechanism to condense, contextualize, and route critical information, the metro risks slower decision cycles, avoidable operating costs, diminished service reliability, and heightened safety and legal vulnerabilities. The challenge, therefore, is to equip every stakeholder—from station controllers and rolling-stock engineers to finance officers and executive directors—with rapid, trustworthy snapshots of the documents that matter to them, while preserving traceability to the original source. Solving this problem will unlock faster cross-department coordination, strengthen regulatory compliance, safeguard institutional knowledge, and ultimately support KMRL’s mission of delivering safe, efficient, and passenger-centric urban transit to Kochi.",
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},
"organization": "Government of Kerala",
"problem_description": "Since its first commercial run in 2017, KMRL has grown into a complex, multidisciplinary enterprise that stretches far beyond train operations. Every business day the organization generates and receives thousands of pages of material: engineering drawings, maintenance job cards, incident reports, vendor invoices, purchase-order correspondence, regulatory directives, environmental-impact studies, safety circulars, HR policies, legal opinions, and board-meeting minutes. These arrive through e-mail, Maximo exports, SharePoint repositories, WhatsApp PDFs, hard-copy scans, and ad-hoc cloud links—often in both English and Malayalam, sometimes in bilingual hybrids, frequently with embedded tables, photos, or signatures. The sheer diversity and volume have created a silent productivity tax: ● Information latency: Front-line managers spend hours skimming lengthy documents for the few actionable lines that affect their shift, delaying decisions on train availability, contractor payments, or staffing reallocations. ● Siloed awareness: Procurement may negotiate a spare-parts contract without realizing that Engineering has already flagged an upcoming design change HR may schedule refresher training unaware of a new safety bulletin released the previous evening. ● Compliance exposure: Regulatory updates from the Commissioner of Metro Rail Safety and the Ministry of Housing & Urban Affairs are buried in inboxes, risking missed deadlines or audit non-conformities. ● Knowledge attrition: Institutional memory remains locked in static files when key personnel transfer or retire, hard-won insights vanish with them. ● Duplicated effort: Different teams independently create summaries or slide decks of the same source documents, multiplying manual work and version-control headaches. As KMRL prepares to expand its corridor, add two new depots, and integrate emerging technologies such as Unified Namespace (UNS) data streams and IoT condition monitoring, the documentary burden will only intensify. Without an organization-wide mechanism to condense, contextualize, and route critical information, the metro risks slower decision cycles, avoidable operating costs, diminished service reliability, and heightened safety and legal vulnerabilities. The challenge, therefore, is to equip every stakeholder—from station controllers and rolling-stock engineers to finance officers and executive directors—with rapid, trustworthy snapshots of the documents that matter to them, while preserving traceability to the original source. Solving this problem will unlock faster cross-department coordination, strengthen regulatory compliance, safeguard institutional knowledge, and ultimately support KMRL’s mission of delivering safe, efficient, and passenger-centric urban transit to Kochi.",
"ps_number": "SIH25080",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "Document Overload at Kochi Metro Rail Limited (KMRL)-An automated solution"
}
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{
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"introduction": "Kochi Metro must decide every night which of its 25 four-car trainsets will enter revenue service at dawn, which remain on standby, and which are held back in the Inspection Bay Line (IBL) for maintenance. The decision hinges on six inter-dependent variables: 1. Fitness Certificates – validity windows issued by Rolling-Stock, Signalling and Telecom departments. 2. Job-Card Status – open vs. closed work orders exported from IBM Maximo. 3. Branding Priorities – contractual commitments that dictate exterior wrap exposure hours. 4. Mileage Balancing – kilometre allocation to equalise bogie, brake-pad and HVAC wear. 5. Cleaning & Detailing Slots – available manpower and bay occupancy for interior deep-cleaning. 6. Stabling Geometry – physical bay positions that minimise nightly shunting and morning turn-out time. At present these data points reside in siloed spreadsheets, manual logbooks, and daily WhatsApp updates. Supervisors reconcile them in a time-compressed window (21:00–23:00 IST) using ad-hoc filters and experience-based heuristics. The process is opaque, non-repeatable, and highly error-prone: ● Missing a single telecom clearance can force an unscheduled rake withdrawal, eroding the 99.5 % punctuality KPI. ● Uneven mileage assignment accelerates component fatigue, inflating maintenance cost. ● Inadequate visibility into branding priorities risks breaching advertiser SLAs, exposing KMRL to revenue penalties. ● Excessive night-time shunting to rearrange rakes increases energy consumption and track-occupancy safety risk. With fleet size slated to grow to 40 trainsets and two depots by 2027, the existing manual workflow cannot scale linearly—neither in staffing nor in cognitive load. Therefore, KMRL requires an integrated, algorithm-driven decision-support platform that can: ● Ingest heterogeneous inputs (Maximo exports, IoT fitness sensors, UNS streams, manual overrides) in near-real-time. ● Enforce rule-based constraints and multi-objective optimisation (service readiness, reliability, cost, branding exposure). ● Generate a ranked induction list with explainable reasoning, conflict alerts, and “what-if” simulation. ● Learn from historical outcomes via machine-learning feedback loops to improve forecast accuracy over time. Such a system will transform induction planning from a manual reconciliation exercise into a reproducible, auditable, data-driven process—delivering higher fleet availability, lower lifecycle cost, and an enhanced passenger experience while freeing operations staff to focus on strategic exceptions rather than routine data wrangling.",
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"organization": "Government of Kerala",
"problem_description": "Kochi Metro must decide every night which of its 25 four-car trainsets will enter revenue service at dawn, which remain on standby, and which are held back in the Inspection Bay Line (IBL) for maintenance. The decision hinges on six inter-dependent variables: 1. Fitness Certificates – validity windows issued by Rolling-Stock, Signalling and Telecom departments. 2. Job-Card Status – open vs. closed work orders exported from IBM Maximo. 3. Branding Priorities – contractual commitments that dictate exterior wrap exposure hours. 4. Mileage Balancing – kilometre allocation to equalise bogie, brake-pad and HVAC wear. 5. Cleaning & Detailing Slots – available manpower and bay occupancy for interior deep-cleaning. 6. Stabling Geometry – physical bay positions that minimise nightly shunting and morning turn-out time. At present these data points reside in siloed spreadsheets, manual logbooks, and daily WhatsApp updates. Supervisors reconcile them in a time-compressed window (21:00–23:00 IST) using ad-hoc filters and experience-based heuristics. The process is opaque, non-repeatable, and highly error-prone: ● Missing a single telecom clearance can force an unscheduled rake withdrawal, eroding the 99.5 % punctuality KPI. ● Uneven mileage assignment accelerates component fatigue, inflating maintenance cost. ● Inadequate visibility into branding priorities risks breaching advertiser SLAs, exposing KMRL to revenue penalties. ● Excessive night-time shunting to rearrange rakes increases energy consumption and track-occupancy safety risk. With fleet size slated to grow to 40 trainsets and two depots by 2027, the existing manual workflow cannot scale linearly—neither in staffing nor in cognitive load. Therefore, KMRL requires an integrated, algorithm-driven decision-support platform that can: ● Ingest heterogeneous inputs (Maximo exports, IoT fitness sensors, UNS streams, manual overrides) in near-real-time. ● Enforce rule-based constraints and multi-objective optimisation (service readiness, reliability, cost, branding exposure). ● Generate a ranked induction list with explainable reasoning, conflict alerts, and “what-if” simulation. ● Learn from historical outcomes via machine-learning feedback loops to improve forecast accuracy over time. Such a system will transform induction planning from a manual reconciliation exercise into a reproducible, auditable, data-driven process—delivering higher fleet availability, lower lifecycle cost, and an enhanced passenger experience while freeing operations staff to focus on strategic exceptions rather than routine data wrangling.",
"ps_number": "SIH25081",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "AI-Driven Train Induction Planning & Scheduling for Kochi Metro Rail Limited (KMRL)"
}
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"introduction": "NATPAC is into transportation planning but accurate data collection is a tideous task. Household data collection using manual survey is also time consuming and does not cover even a small percentage of the population making any planning exercise inadequate. Therefore, the problem statement covers development of a travel related software app that can be installed on mobile phones that could capture trip related information - trip number, origin, time, mode, destination etc - such that a travel related activity and trip chain can be established. Also the number and details of the accompanying travellers also can be captured if possible. Consent from the user is required. Some of these details can be automatically detected and some details can be nudged to be filled by the user. Once these trip details are complete, it can be saved in a server/ database which can be accessed by NATPAC Scientists for planning purposes.",
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},
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"problem_description": "NATPAC is into transportation planning but accurate data collection is a tideous task. Household data collection using manual survey is also time consuming and does not cover even a small percentage of the population making any planning exercise inadequate. Therefore, the problem statement covers development of a travel related software app that can be installed on mobile phones that could capture trip related information - trip number, origin, time, mode, destination etc - such that a travel related activity and trip chain can be established. Also the number and details of the accompanying travellers also can be captured if possible. Consent from the user is required. Some of these details can be automatically detected and some details can be nudged to be filled by the user. Once these trip details are complete, it can be saved in a server/ database which can be accessed by NATPAC Scientists for planning purposes.",
"ps_number": "SIH25082",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Travel & Tourism",
"title": "Development of a travel related software app that can be installed on mobile phones that could capture trip related information"
}
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"introduction": "Kerala hosts a significant migrant population lacking comprehensive health record systems. These individuals often serve as a carrier for infectious diseases, posing serious public health risks to local communities. A dedicated software solution for maintaining migrant health records would support SDG achievement, prevent disease transmission and enhance public health surveillance and assist in elimination of diseases while ensuring fair and impartial healthcare access.",
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"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Kerala",
"problem_description": "Kerala hosts a significant migrant population lacking comprehensive health record systems. These individuals often serve as a carrier for infectious diseases, posing serious public health risks to local communities. A dedicated software solution for maintaining migrant health records would support SDG achievement, prevent disease transmission and enhance public health surveillance and assist in elimination of diseases while ensuring fair and impartial healthcare access.",
"ps_number": "SIH25083",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "MedTech / BioTech / HealthTech",
"title": "Digital Health Record Management System for migrant workers in Kerala aligned with sustainable development goals"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "India's substantial chronic kidney disease patient population relies on dialysis services available from sub divisional level hospitals to specialist Centres including mobile units. Current dialysis infrastructure lacks earthquake resistant technology, potentially resulting in treatment interruption and patient mortality during seismic events. A stabilisation system would ensure continuous dialysis delivery during earthquake, safeguarding patient's life",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Kerala",
"problem_description": "India's substantial chronic kidney disease patient population relies on dialysis services available from sub divisional level hospitals to specialist Centres including mobile units. Current dialysis infrastructure lacks earthquake resistant technology, potentially resulting in treatment interruption and patient mortality during seismic events. A stabilisation system would ensure continuous dialysis delivery during earthquake, safeguarding patient's life",
"ps_number": "SIH25084",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Disaster Management",
"title": "Earthquake stabilised dialysis system for patient safety during seismic events"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
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"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "Solution for NON-REVENUE LOSS IN WATER SUPPLY Solution to improve AWARENESS IN WATER CONSERVATION TREATMENT OF WASTE WATER AND REUSE FOR DOMESTIC PURPOSES",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Kerala",
"problem_description": "Solution for NON-REVENUE LOSS IN WATER SUPPLY Solution to improve AWARENESS IN WATER CONSERVATION TREATMENT OF WASTE WATER AND REUSE FOR DOMESTIC PURPOSES",
"ps_number": "SIH25085",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Miscellaneous",
"title": "Solution for NON-REVENUE LOSS IN WATER SUPPLY Solution to improve AWARENESS IN WATER CONSERVATION TREATMENT OF WASTE WATER AND REUSE FOR DOMESTIC PURPOSES"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "GREY WATER MANAGEMENT AND REUSE WET LAND MANAGEMENT WATER CONSERVATION",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Kerala",
"problem_description": "GREY WATER MANAGEMENT AND REUSE WET LAND MANAGEMENT WATER CONSERVATION",
"ps_number": "SIH25090",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Clean & Green Technology",
"title": "GREY WATER MANAGEMENT AND REUSE WET LAND MANAGEMENT WATER CONSERVATION"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
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"eligibility": null,
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"introduction": "Background:\nWith the implementation of the National Education Policy (NEP) 2020, all higher education institutions-including Institutes of Teacher Education and general degree colleges-have transitioned to Four-Year Undergraduate Programmes (FYUP) and integrated teacher education programs like B.Ed., M.Ed., and ITEP. These programs feature flexible, creditbased, multidisciplinary structures allowing students to choose Major, Minor, Skill-Based, Ability Enhancement, and Value-Added courses. Manual timetable creation under this new framework has become extremely complex, involving numerous subject combinations, varying credit hours, student preferences, and faculty workload distribution. To manage this, colleges usually form dedicated timetable committees, but even then, clashes, underutilization of faculty, and scheduling inefficiencies persist.\n\nDescription:\nThis problem statement envisions the development of a web-based or hybrid intelligent system that can generate automated, conflict-free, optimized academic timetables alignment with the NEP 2020 course structure. The system must integrate:\n- Student data (elective choices, enrolled credits)\n- Curriculum structure (course codes, credits, theory/practical split)\n- Faculty workload, availability, and expertise\n- Room/lab availability and capacity\n- Teaching practice schedules (especially relevant for B.Ed. and M.Ed.)\n- Field work, internships, and project components\nThe system should also allow dynamic editing, scenario simulation, and scalability for upcoming semesters. A user-friendly admin interface is essential to allow real-time inputs and updates.\n\nExpected Solution:\nA deployable AI/ML-assisted Timetable Generator that:\n- Accepts structured inputs from academic, student, and faculty databases\n- Generates a semester-wise timetable for multiple programs (B.Ed., M.Ed., FYUP, ITEP)\n- Prevents scheduling conflicts across faculty and infrastructure\n- Accommodates future course additions and changing NEP guidelines\n- Offers exportable formats (PDF, Excel) for student and faculty sharing\n- Can be integrated with existing Academic Management Systems",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Jammu and Kashmir",
"problem_description": "Background:\nWith the implementation of the National Education Policy (NEP) 2020, all higher education institutions-including Institutes of Teacher Education and general degree colleges-have transitioned to Four-Year Undergraduate Programmes (FYUP) and integrated teacher education programs like B.Ed., M.Ed., and ITEP. These programs feature flexible, creditbased, multidisciplinary structures allowing students to choose Major, Minor, Skill-Based, Ability Enhancement, and Value-Added courses. Manual timetable creation under this new framework has become extremely complex, involving numerous subject combinations, varying credit hours, student preferences, and faculty workload distribution. To manage this, colleges usually form dedicated timetable committees, but even then, clashes, underutilization of faculty, and scheduling inefficiencies persist.\n\nDescription:\nThis problem statement envisions the development of a web-based or hybrid intelligent system that can generate automated, conflict-free, optimized academic timetables alignment with the NEP 2020 course structure. The system must integrate:\n- Student data (elective choices, enrolled credits)\n- Curriculum structure (course codes, credits, theory/practical split)\n- Faculty workload, availability, and expertise\n- Room/lab availability and capacity\n- Teaching practice schedules (especially relevant for B.Ed. and M.Ed.)\n- Field work, internships, and project components\nThe system should also allow dynamic editing, scenario simulation, and scalability for upcoming semesters. A user-friendly admin interface is essential to allow real-time inputs and updates.\n\nExpected Solution:\nA deployable AI/ML-assisted Timetable Generator that:\n- Accepts structured inputs from academic, student, and faculty databases\n- Generates a semester-wise timetable for multiple programs (B.Ed., M.Ed., FYUP, ITEP)\n- Prevents scheduling conflicts across faculty and infrastructure\n- Accommodates future course additions and changing NEP guidelines\n- Offers exportable formats (PDF, Excel) for student and faculty sharing\n- Can be integrated with existing Academic Management Systems",
"ps_number": "SIH25091",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "AI-Based Timetable Generation System aligned with NEP 2020 for Multidisciplinary Education Structures"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
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"deliverables": null,
"description": null,
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"eligibility": null,
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"introduction": "Problem Statement:\n\nContext:\nMental health issues among college students have significantly increased in recent years, including anxiety, depression, burnout, sleep disorders, academic stress, and social isolation. However, there is a major gap in the availability, accessibility, and stigma-free delivery of mental health support in most higher education institutions, especially in rural and semi-urban colleges.\n\nProblem Faced:\n- Absence of a structured, scalable, and stigma-free psychological intervention system.\n- Lack of early detection and preventive mental health tools.\n- Under-utilization of college counselling centres due to fear of judgment or lack of awareness.\n- No centralized mental health monitoring or data-driven policy framework within institutions.\n\nProposed Technological Challenge:\nDevelop a Digital Psychological Intervention System (web-based and/or mobile app) with the following capabilities:\n1. AI-guided First-Aid Support: Interactive chat box that offers coping strategies and refers students to professionals when needed.\n2. Confidential Booking System: For appointment with on-campus counsellor or mental health helpline.\n3. Psychoeducational Resource Hub: Videos, relaxation audio, mental wellness guides in regional languages.\n4. Peer Support Platform: Moderated peer-to-peer support forum with trained student volunteers.\n5. Admin Dashboard: Anonymous data analytics for authorities to recognize trends and plan interventions.\n\nDepartment/Section Owning the Problem:\nDepartment of Student Welfare / Department of Psychology / Internal Quality Assurance Cell (IQAC).\n\nProblem Explanation Video:\nTo be prepared by students.\n\nNeed for Digital Platform for Psychological Support:\nMost available apps are generic, Western-oriented, or paid. They do not integrate:\n• Regional cultural context and language\n• Institution-specific customization\n• Offline support mapping (e.g., linking with college counsellors)\n• Real-time analytics for admin\nHence, a tailored open-source solution is needed.\n\nSample Data/Data Structures for Participants:\n• Level of problem through standard psychological screening tools (PHQ-9 / GAD-7 / GHQ, etc.)\n• Mock student profiles (anonymized)\n• Institutional support structure (counsellor availability, resources, helpline)",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Jammu and Kashmir",
"problem_description": "Problem Statement:\n\nContext:\nMental health issues among college students have significantly increased in recent years, including anxiety, depression, burnout, sleep disorders, academic stress, and social isolation. However, there is a major gap in the availability, accessibility, and stigma-free delivery of mental health support in most higher education institutions, especially in rural and semi-urban colleges.\n\nProblem Faced:\n- Absence of a structured, scalable, and stigma-free psychological intervention system.\n- Lack of early detection and preventive mental health tools.\n- Under-utilization of college counselling centres due to fear of judgment or lack of awareness.\n- No centralized mental health monitoring or data-driven policy framework within institutions.\n\nProposed Technological Challenge:\nDevelop a Digital Psychological Intervention System (web-based and/or mobile app) with the following capabilities:\n1. AI-guided First-Aid Support: Interactive chat box that offers coping strategies and refers students to professionals when needed.\n2. Confidential Booking System: For appointment with on-campus counsellor or mental health helpline.\n3. Psychoeducational Resource Hub: Videos, relaxation audio, mental wellness guides in regional languages.\n4. Peer Support Platform: Moderated peer-to-peer support forum with trained student volunteers.\n5. Admin Dashboard: Anonymous data analytics for authorities to recognize trends and plan interventions.\n\nDepartment/Section Owning the Problem:\nDepartment of Student Welfare / Department of Psychology / Internal Quality Assurance Cell (IQAC).\n\nProblem Explanation Video:\nTo be prepared by students.\n\nNeed for Digital Platform for Psychological Support:\nMost available apps are generic, Western-oriented, or paid. They do not integrate:\n• Regional cultural context and language\n• Institution-specific customization\n• Offline support mapping (e.g., linking with college counsellors)\n• Real-time analytics for admin\nHence, a tailored open-source solution is needed.\n\nSample Data/Data Structures for Participants:\n• Level of problem through standard psychological screening tools (PHQ-9 / GAD-7 / GHQ, etc.)\n• Mock student profiles (anonymized)\n• Institutional support structure (counsellor availability, resources, helpline)",
"ps_number": "SIH25092",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "MedTech / BioTech / HealthTech",
"title": "Development of a Digital Mental Health and Psychological Support System for Students in Higher Education"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
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"deliverables": null,
"description": null,
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"eligibility": null,
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"introduction": "Background:\nDespite increasing digitization in education, student achievements ranging from academic excellence to participation in curricular and extracurricular activities remain scattered across various departments or lost in paper-based records, in many colleges and universities. There is no centralized digital platform that enables institutions to document, track, and showcase a student's complete profile, including:\n- Conferences and workshops attended\n- Certifications earned\n- Club activities and volunteering efforts\n- Competitions and academic contests\n- Leadership roles and internships\n- Community services done\n\nThis gap not only hampers institutional efficiency but also limits students' ability to build a verified and dynamic portfolio that could support job applications, higher education admissions, and skill recognition. In addition, during events like NAAC accreditation, the lack of consolidated data becomes a major administrative challenge.\n\nDescription:\nThe above problem necessitates the development of a Smart Student Hub (Mobile + Web Application) that acts as a centralized student record and achievement management platform.\n\nKey Features include:\n- Dynamic Student Dashboard: Real-time updates on academic performance, attendance, and credit-based activities.\n- Activity Tracker: Upload and validate participation in seminars, conferences, online courses (e.g., MOOCs), internships, and extra-curriculars.\n- Faculty Approval Panel: Faculty or admin can approve uploaded records to maintain credibility.\n- Auto-Generated Digital Portfolio: Downloadable and shareable verified student portfolio in PDF or web link format.\n- Analytics & Reporting: For institutions to generate reports for NAAC, AICTE, NIRF, or internal evaluations.\n- Integration Support: Can link with existing Learning Management Systems (LMS), ERP, or digital university platforms.\n\nImpact and Benefits:\n- Empowers students with a verified, holistic digital profile.\n- Facilitates career planning, placements, and scholarship/higher studies applications.\n- Reduces administrative burden during audits and accreditations.\n- Encourages participation in co-curricular activities by making achievements visible and valued.\n- Promotes data-driven decision-making at the institutional level.\n\nExpected Solution:\nWe propose the development of a mobile + web-based application that can digitally catalogue and manage a student's academic and non-academic achievements throughout their time in college. The proposed protocol will:\n- Build a verified, holistic student profile from Day 1\n- Reduce paperwork and improve transparency\n- Make students better prepared for placements, fellowships, or postgraduate admissions\n- Empower faculty with real-time data for mentoring and tracking\n- Align with the digital transformation goals of higher education institutions\n- Enhance institutional efficiency during accreditation or policy audits\n\nThis will bridge the gap by offering an all-in-one solution that empowers students, simplifies faculty tasks, and modernizes institutional operations.",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Jammu and Kashmir",
"problem_description": "Background:\nDespite increasing digitization in education, student achievements ranging from academic excellence to participation in curricular and extracurricular activities remain scattered across various departments or lost in paper-based records, in many colleges and universities. There is no centralized digital platform that enables institutions to document, track, and showcase a student's complete profile, including:\n- Conferences and workshops attended\n- Certifications earned\n- Club activities and volunteering efforts\n- Competitions and academic contests\n- Leadership roles and internships\n- Community services done\n\nThis gap not only hampers institutional efficiency but also limits students' ability to build a verified and dynamic portfolio that could support job applications, higher education admissions, and skill recognition. In addition, during events like NAAC accreditation, the lack of consolidated data becomes a major administrative challenge.\n\nDescription:\nThe above problem necessitates the development of a Smart Student Hub (Mobile + Web Application) that acts as a centralized student record and achievement management platform.\n\nKey Features include:\n- Dynamic Student Dashboard: Real-time updates on academic performance, attendance, and credit-based activities.\n- Activity Tracker: Upload and validate participation in seminars, conferences, online courses (e.g., MOOCs), internships, and extra-curriculars.\n- Faculty Approval Panel: Faculty or admin can approve uploaded records to maintain credibility.\n- Auto-Generated Digital Portfolio: Downloadable and shareable verified student portfolio in PDF or web link format.\n- Analytics & Reporting: For institutions to generate reports for NAAC, AICTE, NIRF, or internal evaluations.\n- Integration Support: Can link with existing Learning Management Systems (LMS), ERP, or digital university platforms.\n\nImpact and Benefits:\n- Empowers students with a verified, holistic digital profile.\n- Facilitates career planning, placements, and scholarship/higher studies applications.\n- Reduces administrative burden during audits and accreditations.\n- Encourages participation in co-curricular activities by making achievements visible and valued.\n- Promotes data-driven decision-making at the institutional level.\n\nExpected Solution:\nWe propose the development of a mobile + web-based application that can digitally catalogue and manage a student's academic and non-academic achievements throughout their time in college. The proposed protocol will:\n- Build a verified, holistic student profile from Day 1\n- Reduce paperwork and improve transparency\n- Make students better prepared for placements, fellowships, or postgraduate admissions\n- Empower faculty with real-time data for mentoring and tracking\n- Align with the digital transformation goals of higher education institutions\n- Enhance institutional efficiency during accreditation or policy audits\n\nThis will bridge the gap by offering an all-in-one solution that empowers students, simplifies faculty tasks, and modernizes institutional operations.",
"ps_number": "SIH25093",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Centralised Digital Platform for Comprehensive student activity record in HEIs"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": "The decline in student enrollment in government degree colleges is not a consequence of poor infrastructure or lack of teaching resources. At the heart of the problem lies a critical gap in awareness, most students and often their parents do not understand the importance of graduation, what career opportunities different degree courses unlock, or how to choose a subject stream based on personal interest and future prospects. After passing Class 10 or 12, students face confusion about:\n- Which subject stream (Arts, Science, Commerce) to opt for.\n- What kind of degree programs are available in nearby government colleges.\n- What jobs or higher studies they can pursue after choosing a particular course.\n- Whether pursuing graduation is even worth the effort, especially when short-term job or skill-based courses seem more accessible.\n\nThis lack of clarity leads to poor academic decisions, dropouts, or migration to private institutions that often promise but fail to deliver quality education.",
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": "The above problem statement envisages that a customized Digital Guidance Platform for students should be developed as a one-stop personalized career and education advisor.\n\nKey Features of the App/Web Platform:\n- Aptitude & Interest-Based Course Suggestion: Short quizzes to assess the student's interests, strengths and personality traits. Based on results, the app will suggest suitable streams and subjects (Arts, Science, Commerce, Vocational). Students can compare career paths based on different combinations.\n- Course-to-Career Path Mapping: Detailed visual charts showing what each degree (B.A., B.Sc., B.Com., BBA, etc.) offers. The industries or sectors each course leads to and relevant government exams, private jobs, entrepreneurial options, or higher education available after each stream.\n- Nearby Government Colleges Directory: Location-based listing of government colleges, information on available degree programs; cut-offs and eligibility, medium of instruction, facilities (hostel, lab, library, internet access).\n- Timeline Tracker: Notification system for admission dates, scholarship application windows, college entrance tests or counseling schedules.\n- Customization and Personalization: User profiles created at login — tracking age, gender, class, academic interests. AI-driven recommendation engine suggests:\n • Courses to apply for.\n • Colleges nearby.\n • Career paths aligned with their aptitude.\n • Study materials tailored to subject choice.\n\nImplementation Strategy\n- Stakeholder Collaboration: Involve government education departments, school teachers, NGOs, and counselors for content and outreach.\n- Technology Development: Partner with EdTech developers to build a scalable, lightweight app with offline features for poor internet areas.\n- Pilot Launch: Start in one or two districts with low college enrollment. Get feedback from students and teachers.\n- Full-Scale Rollout: Roll out state-wide or nationally through government schools and skill centers.\n- Monitoring & Feedback: Real-time analytics to track usage, app suggestions, successful transitions to college enrollment.",
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": "We propose the development of a mobile + web-based application that acts as a one-stop personalized career and education advisor. The platform will be designed for students guiding them in choosing the right subject combination after class 12th, selecting suitable degree courses in local government colleges, understanding the long-term outcomes of different courses (jobs, entrance exams, skill development) and accessing open-source e-books, skill materials, and scholarships.",
"impact": "- Improved enrollment in government degree colleges by helping students make informed academic decisions.\n- Reduced dropouts after Class 10 and 12.\n- Empowered students with access to reliable, localized career guidance.\n- Stronger perception of government colleges as viable career-building institutions.",
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": null,
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Jammu and Kashmir",
"problem_description": "Background\n\nThe decline in student enrollment in government degree colleges is not a consequence of poor infrastructure or lack of teaching resources. At the heart of the problem lies a critical gap in awareness, most students and often their parents do not understand the importance of graduation, what career opportunities different degree courses unlock, or how to choose a subject stream based on personal interest and future prospects. After passing Class 10 or 12, students face confusion about:\n- Which subject stream (Arts, Science, Commerce) to opt for.\n- What kind of degree programs are available in nearby government colleges.\n- What jobs or higher studies they can pursue after choosing a particular course.\n- Whether pursuing graduation is even worth the effort, especially when short-term job or skill-based courses seem more accessible.\n\nThis lack of clarity leads to poor academic decisions, dropouts, or migration to private institutions that often promise but fail to deliver quality education.\n\nDescription\n\nThe above problem statement envisages that a customized Digital Guidance Platform for students should be developed as a one-stop personalized career and education advisor.\n\nKey Features of the App/Web Platform:\n- Aptitude & Interest-Based Course Suggestion: Short quizzes to assess the student's interests, strengths and personality traits. Based on results, the app will suggest suitable streams and subjects (Arts, Science, Commerce, Vocational). Students can compare career paths based on different combinations.\n- Course-to-Career Path Mapping: Detailed visual charts showing what each degree (B.A., B.Sc., B.Com., BBA, etc.) offers. The industries or sectors each course leads to and relevant government exams, private jobs, entrepreneurial options, or higher education available after each stream.\n- Nearby Government Colleges Directory: Location-based listing of government colleges, information on available degree programs; cut-offs and eligibility, medium of instruction, facilities (hostel, lab, library, internet access).\n- Timeline Tracker: Notification system for admission dates, scholarship application windows, college entrance tests or counseling schedules.\n- Customization and Personalization: User profiles created at login — tracking age, gender, class, academic interests. AI-driven recommendation engine suggests:\n • Courses to apply for.\n • Colleges nearby.\n • Career paths aligned with their aptitude.\n • Study materials tailored to subject choice.\n\nImplementation Strategy\n- Stakeholder Collaboration: Involve government education departments, school teachers, NGOs, and counselors for content and outreach.\n- Technology Development: Partner with EdTech developers to build a scalable, lightweight app with offline features for poor internet areas.\n- Pilot Launch: Start in one or two districts with low college enrollment. Get feedback from students and teachers.\n- Full-Scale Rollout: Roll out state-wide or nationally through government schools and skill centers.\n- Monitoring & Feedback: Real-time analytics to track usage, app suggestions, successful transitions to college enrollment.\n\nExpected Solution\n\nWe propose the development of a mobile + web-based application that acts as a one-stop personalized career and education advisor. The platform will be designed for students guiding them in choosing the right subject combination after class 12th, selecting suitable degree courses in local government colleges, understanding the long-term outcomes of different courses (jobs, entrance exams, skill development) and accessing open-source e-books, skill materials, and scholarships.\n\nImpact\n- Improved enrollment in government degree colleges by helping students make informed academic decisions.\n- Reduced dropouts after Class 10 and 12.\n- Empowered students with access to reliable, localized career guidance.\n- Stronger perception of government colleges as viable career-building institutions.",
"ps_number": "SIH25094",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "One-Stop Personalized Career & Education Advisor"
}
|
{
"category": "Hardware",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
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"introduction": "The student's focus should be on using Fusion's additive manufacturing capabilities. They should aim to learn about additive manufacturing and its applications in aerospace design. This includes studying how to design aerospace components with considerations for structural integrity, and weight reduction. They should define project objectives and design constraints and then utilize generative design tools in Fusion to explore and generate optimized designs. The student should evaluate these designs using simulation and analysis tools within Fusion. They should refine and iterate on the designs to further enhance performance. Finally, they should prototype and test the finalized design using appropriate 3D printing technologies.",
"iot_integration_optional": null,
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},
"organization": "Autodesk",
"problem_description": "The student's focus should be on using Fusion's additive manufacturing capabilities. They should aim to learn about additive manufacturing and its applications in aerospace design. This includes studying how to design aerospace components with considerations for structural integrity, and weight reduction. They should define project objectives and design constraints and then utilize generative design tools in Fusion to explore and generate optimized designs. The student should evaluate these designs using simulation and analysis tools within Fusion. They should refine and iterate on the designs to further enhance performance. Finally, they should prototype and test the finalized design using appropriate 3D printing technologies.",
"ps_number": "SIH25095",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Research and redesign a sport equipment commonly found in sport industry and utilize Fusion software to reimagine its design. Students can use Fusion Features such as Generative Design Topology Optimization Additive Build etc. The redesigned component should showcase innovation enhanced functionality and improved efficiency all while being optimized for 3D printing"
}
|
{
"category": "Hardware",
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"introduction": "Computer-Aided Manufacturing (CAM) is the use of software and computer-controlled machinery to automate a manufacturing process. Based on that definition, you need three components for a CAM system to function: Software that tells a machine how to make a product by generating toolpaths. Machinery that can turn raw material into a finished product. Post Processing converts toolpaths into a language machine can understand. From high efficiency roughing with Adaptive Clearing to simplified control of multi-axis machines with Tool Orientation.",
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},
"organization": "Autodesk",
"problem_description": "Computer-Aided Manufacturing (CAM) is the use of software and computer-controlled machinery to automate a manufacturing process. Based on that definition, you need three components for a CAM system to function: Software that tells a machine how to make a product by generating toolpaths. Machinery that can turn raw material into a finished product. Post Processing converts toolpaths into a language machine can understand. From high efficiency roughing with Adaptive Clearing to simplified control of multi-axis machines with Tool Orientation.",
"ps_number": "SIH25096",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Students are supposed to use Fusion software to generate NC code with machine details & tool library for any industrial component. Students should possess technical skills in areas such as CAD/CAM software G-code programming toolpath optimization and machining fundamentals. Additionally their project ideas should demonstrate a viable solution to a real-world problem ensuring feasibility and practicality in implementation"
}
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"introduction": "Design an autonomous small precision focused machine for planting crops or weeding to automate the task of planting and weeding. These machines are particularly useful to optimize the crop production. It can be autonomous or remote-controlled. The machine needs to be lightweight and flexible for easy transportation. The specifications are as follows: Length x Width x Height - 1000mm x 800 mm x 700 mm Weight — 120 kg Crop Compatibility: rice or onion Project submission must include conceptual sketches and images in the PPT presentation. At least one component must be optimized using the Generative Design module. The robot should incorporate Industry 4.0 applications, such as IoT and AI, for a smart and efficient agriculture innovation solution.",
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},
"organization": "Autodesk",
"problem_description": "Design an autonomous small precision focused machine for planting crops or weeding to automate the task of planting and weeding. These machines are particularly useful to optimize the crop production. It can be autonomous or remote-controlled. The machine needs to be lightweight and flexible for easy transportation. The specifications are as follows: Length x Width x Height - 1000mm x 800 mm x 700 mm Weight — 120 kg Crop Compatibility: rice or onion Project submission must include conceptual sketches and images in the PPT presentation. At least one component must be optimized using the Generative Design module. The robot should incorporate Industry 4.0 applications, such as IoT and AI, for a smart and efficient agriculture innovation solution.",
"ps_number": "SIH25097",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Research and develop a design on autonomous small precision focused machine for planting crops or weeding"
}
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{
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"introduction": "The goal is to develop a coordinated, clash free, BIM model suitable for further use in MEP design, quantity take off, and construction documentation. To generate 2D structural drawings for various members like Beams, Columns, Slabs with detailing.Rendering Quality of the model will be evaluated. Walkthrough presentation of the model should be created of 30 secs. The model should have all the required members (Beams, Columns, Slabs, Stairs, Tile flooring).",
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"organization": "Autodesk",
"problem_description": "The goal is to develop a coordinated, clash free, BIM model suitable for further use in MEP design, quantity take off, and construction documentation. To generate 2D structural drawings for various members like Beams, Columns, Slabs with detailing.Rendering Quality of the model will be evaluated. Walkthrough presentation of the model should be created of 30 secs. The model should have all the required members (Beams, Columns, Slabs, Stairs, Tile flooring).",
"ps_number": "SIH25098",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Students are tasked with designing a 4-story commercial office building using Revit Architecture and Revit Structure ensuring effective integration between architectural and structural models in a BIM environment.(The size of the plot can be assumed by the students all the dimensions wherever necessary can be assumed by students in mm units.)"
}
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"introduction": "Agriculture faces growing threats from soil degradation, unpredictable weather, and pest outbreaks, leading to reduced yields and economic losses. Traditional monitoring methods are often delayed, labor-intensive, and lack precision. There is a need for a unified software platform that integrates remote sensing and sensor data to provide timely, field-level insights on crop health, soil conditions, and pest risks using AI-driven analysis. Combining spectral imaging and environmental data enables early detection and targeted action, helping farmers shift from reactive to proactive crop management. The solution is an AI-powered platform built using Hyperspectral Imaging Library (https://in.mathworks.com/matlabcentral/fileexchange/76796- hyperspectral-imaging-library-for-image-processing-toolbox), Image Processing Toolbox (https://in.mathworks.com/products/image-Agriculture faces growing threats from soil degradation, unpredictable weather, and pest outbreaks, leading to reduced yields and economic losses. Traditional monitoring methods are often delayed, labor-intensive, and lack precision.There is a need for a unified software platform that integrates remote sensing and sensor data to provide timely, field-level insights on crop health, soil conditions, and pest risks using AI-driven analysis. Combining spectral imaging and environmental data enables early detection and targeted action,helping farmers shift from reactive to proactive crop management. The solution is an AI-powered platform built using Hyperspectral Imaging Library (https://in.mathworks.com/matlabcentral/fileexchange/76796- hyperspectral-imaging-library-for-image-processing-toolbox), Image Processing Toolbox (https://in.mathworks.com/products/image- processing.html ) and Deep Learning Toolbox (https://in.mathworks.com/products/deep-learning.html ). It ingests and aligns multispectral/hyperspectral image sequences with historical datasets, extracts vegetation and soil indices, and applies models like LSTM and CNN to detect trends and predict vegetation stress or disease risk. Environmental sensor data—such as soil moisture, air temperature, humidity, and leaf wetness—is integrated to contextualize spectral anomalies, improve the accuracy of stress and pest predictions, and trigger zone-specific alerts. Sensor inputs are fused with image-derived features to enhance temporal modeling and identify conditions conducive to pest outbreaks or crop decline. Target users include agronomists, researchers, field technicians, and progressive farmers who require timely, localized insights for monitoring and decision-making. Users interact through an intuitive dashboard that displays spectral health maps, temporal trend plots, anomaly alerts, soil condition summaries, and predicted risk zones. The platform supports continuous learning, localized insights, and outputs reports and mobile- friendly notifications—empowering sustainable, precision agriculture through a scalable AI-driven system. processing.html ) and Deep Learning Toolbox ( https://in.mathworks.com/products/deep-learning.html ). It ingests and aligns multispectral/hyperspectral image sequences with historical datasets, extracts vegetation and soil indices, and applies models like LSTM and CNN to detect trends and predict vegetation stress or disease risk. Environmental sensor data—such as soil moisture, air temperature, humidity, and leaf wetness—is integrated to contextualize spectral anomalies, improve the accuracy of stress and pest predictions, and trigger zone-specific alerts. Sensor inputs are fused with image-derived features to enhance temporal modeling and identify conditions conducive to pest outbreaks or crop decline. Target users include agronomists, researchers, field technicians, and progressive farmers who require timely, localized insights for monitoring and decision-making. Users interact through an intuitive dashboard that displays spectral health maps, temporal trend plots, anomaly alerts, soil condition summaries, and predicted risk zones. The platform supports continuous learning, localized insights, and outputs reports and mobile- friendly notifications—empowering sustainable, precision agriculture through a scalable AI-driven system.",
"iot_integration_optional": null,
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},
"organization": "MathWorks India Pvt. Ltd.",
"problem_description": "Agriculture faces growing threats from soil degradation, unpredictable weather, and pest outbreaks, leading to reduced yields and economic losses. Traditional monitoring methods are often delayed, labor-intensive, and lack precision. There is a need for a unified software platform that integrates remote sensing and sensor data to provide timely, field-level insights on crop health, soil conditions, and pest risks using AI-driven analysis. Combining spectral imaging and environmental data enables early detection and targeted action, helping farmers shift from reactive to proactive crop management. The solution is an AI-powered platform built using Hyperspectral Imaging Library (https://in.mathworks.com/matlabcentral/fileexchange/76796- hyperspectral-imaging-library-for-image-processing-toolbox), Image Processing Toolbox (https://in.mathworks.com/products/image-Agriculture faces growing threats from soil degradation, unpredictable weather, and pest outbreaks, leading to reduced yields and economic losses. Traditional monitoring methods are often delayed, labor-intensive, and lack precision.There is a need for a unified software platform that integrates remote sensing and sensor data to provide timely, field-level insights on crop health, soil conditions, and pest risks using AI-driven analysis. Combining spectral imaging and environmental data enables early detection and targeted action,helping farmers shift from reactive to proactive crop management. The solution is an AI-powered platform built using Hyperspectral Imaging Library (https://in.mathworks.com/matlabcentral/fileexchange/76796- hyperspectral-imaging-library-for-image-processing-toolbox), Image Processing Toolbox (https://in.mathworks.com/products/image- processing.html ) and Deep Learning Toolbox (https://in.mathworks.com/products/deep-learning.html ). It ingests and aligns multispectral/hyperspectral image sequences with historical datasets, extracts vegetation and soil indices, and applies models like LSTM and CNN to detect trends and predict vegetation stress or disease risk. Environmental sensor data—such as soil moisture, air temperature, humidity, and leaf wetness—is integrated to contextualize spectral anomalies, improve the accuracy of stress and pest predictions, and trigger zone-specific alerts. Sensor inputs are fused with image-derived features to enhance temporal modeling and identify conditions conducive to pest outbreaks or crop decline. Target users include agronomists, researchers, field technicians, and progressive farmers who require timely, localized insights for monitoring and decision-making. Users interact through an intuitive dashboard that displays spectral health maps, temporal trend plots, anomaly alerts, soil condition summaries, and predicted risk zones. The platform supports continuous learning, localized insights, and outputs reports and mobile- friendly notifications—empowering sustainable, precision agriculture through a scalable AI-driven system. processing.html ) and Deep Learning Toolbox ( https://in.mathworks.com/products/deep-learning.html ). It ingests and aligns multispectral/hyperspectral image sequences with historical datasets, extracts vegetation and soil indices, and applies models like LSTM and CNN to detect trends and predict vegetation stress or disease risk. Environmental sensor data—such as soil moisture, air temperature, humidity, and leaf wetness—is integrated to contextualize spectral anomalies, improve the accuracy of stress and pest predictions, and trigger zone-specific alerts. Sensor inputs are fused with image-derived features to enhance temporal modeling and identify conditions conducive to pest outbreaks or crop decline. Target users include agronomists, researchers, field technicians, and progressive farmers who require timely, localized insights for monitoring and decision-making. Users interact through an intuitive dashboard that displays spectral health maps, temporal trend plots, anomaly alerts, soil condition summaries, and predicted risk zones. The platform supports continuous learning, localized insights, and outputs reports and mobile- friendly notifications—empowering sustainable, precision agriculture through a scalable AI-driven system.",
"ps_number": "SIH25099",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "AI-powered monitoring of crop health soil condition and pest risks using multispectral/hyperspectral imaging and sensor data"
}
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"introduction": "Urban traffic congestion in Indian cities is worsened by the limitations of current urban planning software, which often assumes ideal, well-maintained road conditions typical of developed countries. Such software fails to account for the unpredictable and nuanced realities of Indian roads—such as potholes, temporary barricades, partial lane closures, construction activities, and erratic driver behaviors. Manually modeling these complex and dynamic road features into digital twins for realistic traffic simulations is both tedious and time-consuming, often requiring significant engineering effort before any meaningful simulation can be conducted. To address this challenge, MathWorks is seeking innovative solutions to accelerate the creation of highly detailed digital twins of existing Indian road junctions and networks. The aim is to design toolsets, templates, asset libraries, or workflows—leveraging MATLAB, Simulink, Automated Driving Toolbox, RoadRunner, and potentially generative AI—that significantly streamline the process. The solution should enable users to easily incorporate typical Indian road features and simulate hyper-local vehicular behaviors in their modeling, thereby empowering traffic management agencies to run realistic simulations for crisis handling, congestion management, and infrastructure planning. Proposals should focus on enhancing the modeling process rather than traffic optimization and must integrate seamlessly with MATLAB-based simulation workflows. For more information on relevant tools, please refer to the following links: Driving Scenario Designer – Design driving scenarios, configure sensors, and generate synthetic data – MATLAB https://in.mathworks.com/help/driving/ref/drivingscenariodesigner-app.html?requestedDomain= GitHub - MathWorks/OpenTrafficLab https://github.com/mathworks/OpenTrafficLab Create Driving Scenario Variations Programmatically – MATLAB & Simulink https://in.mathworks.com/help/driving/ug/create-driving-scenario-variations-programmatically.html?requestedDomain= Sensor Simulation and Virtual Scene Design with the Driving Scenario Designer App, Part 1 – MATLAB https://in.mathworks.com/videos/driving-scenario-designer-1529302116471.html?s_tid=srchtitle_videos_main_1_driving+scenario Import OpenStreetMap Data into Driving Scenario – MATLAB & Simulink https://in.mathworks.com/help/driving/ug/import-openstreetmap-data-into-driving-scenario.html",
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"organization": "MathWorks India Pvt. Ltd.",
"problem_description": "Urban traffic congestion in Indian cities is worsened by the limitations of current urban planning software, which often assumes ideal, well-maintained road conditions typical of developed countries. Such software fails to account for the unpredictable and nuanced realities of Indian roads—such as potholes, temporary barricades, partial lane closures, construction activities, and erratic driver behaviors. Manually modeling these complex and dynamic road features into digital twins for realistic traffic simulations is both tedious and time-consuming, often requiring significant engineering effort before any meaningful simulation can be conducted. To address this challenge, MathWorks is seeking innovative solutions to accelerate the creation of highly detailed digital twins of existing Indian road junctions and networks. The aim is to design toolsets, templates, asset libraries, or workflows—leveraging MATLAB, Simulink, Automated Driving Toolbox, RoadRunner, and potentially generative AI—that significantly streamline the process. The solution should enable users to easily incorporate typical Indian road features and simulate hyper-local vehicular behaviors in their modeling, thereby empowering traffic management agencies to run realistic simulations for crisis handling, congestion management, and infrastructure planning. Proposals should focus on enhancing the modeling process rather than traffic optimization and must integrate seamlessly with MATLAB-based simulation workflows. For more information on relevant tools, please refer to the following links: Driving Scenario Designer – Design driving scenarios, configure sensors, and generate synthetic data – MATLAB https://in.mathworks.com/help/driving/ref/drivingscenariodesigner-app.html?requestedDomain= GitHub - MathWorks/OpenTrafficLab https://github.com/mathworks/OpenTrafficLab Create Driving Scenario Variations Programmatically – MATLAB & Simulink https://in.mathworks.com/help/driving/ug/create-driving-scenario-variations-programmatically.html?requestedDomain= Sensor Simulation and Virtual Scene Design with the Driving Scenario Designer App, Part 1 – MATLAB https://in.mathworks.com/videos/driving-scenario-designer-1529302116471.html?s_tid=srchtitle_videos_main_1_driving+scenario Import OpenStreetMap Data into Driving Scenario – MATLAB & Simulink https://in.mathworks.com/help/driving/ug/import-openstreetmap-data-into-driving-scenario.html",
"ps_number": "SIH25100",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Accelerating High-Fidelity Road Network Modeling for Indian Traffic Simulations"
}
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"introduction": "Background: Rural diploma colleges often operate without subject Lecturers in various fields such as artificial intelligence, VLSI, or renewable energy. Students must rely on self-study material or travel to cities for coaching,deepening the urban-rural learning divide. Description: The challenge is less about willingness than about infrastructure. Typical village campuses juggle low and unstable data speeds, making conventional video-conferencing unreliable. Previous interventions failed when platforms assumed high-bandwidth links or required complicated equipment. To succeed, a new approach must embrace low-bandwidth realities, prioritise audio quality, compress visual content, and ensure that learning can continue even during connectivity lapses. It should encourage synchronous interaction yet also provide recordings that remain small enough for easy download on limited data plans. Faculty in cities need a simple way to deliver lectures from any quiet corner, while rural students require a user experience that functions on entry-level smartphones. Interactive elements quizzes, polls, discussion boards-must remain functional at low speeds.Crucially, the entire solution should minimise the leaming curve for educators and be financially sustainable for resource-constrained institutes. By offering a design that blends live engagement with asynchronous access, the college can bring expert instruction to every campus without waiting for large capital investments.This is precisely the sort of context-aware challenge that Smart India Hackathon teams can address through innovative but lightweight software solutions. Expected Solution: Student teams should outline a software-only virtual-classroom ecosystem that delivers clear audio and concise visual content on limited bandwidth, supports interactive live as well as recorded sessions, and allows easy content access for learners who may need to download materials during off-peak hours-all without relying on specialised hardware or costly licences.",
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},
"organization": "Government of Rajasthan",
"problem_description": "Background: Rural diploma colleges often operate without subject Lecturers in various fields such as artificial intelligence, VLSI, or renewable energy. Students must rely on self-study material or travel to cities for coaching,deepening the urban-rural learning divide. Description: The challenge is less about willingness than about infrastructure. Typical village campuses juggle low and unstable data speeds, making conventional video-conferencing unreliable. Previous interventions failed when platforms assumed high-bandwidth links or required complicated equipment. To succeed, a new approach must embrace low-bandwidth realities, prioritise audio quality, compress visual content, and ensure that learning can continue even during connectivity lapses. It should encourage synchronous interaction yet also provide recordings that remain small enough for easy download on limited data plans. Faculty in cities need a simple way to deliver lectures from any quiet corner, while rural students require a user experience that functions on entry-level smartphones. Interactive elements quizzes, polls, discussion boards-must remain functional at low speeds.Crucially, the entire solution should minimise the leaming curve for educators and be financially sustainable for resource-constrained institutes. By offering a design that blends live engagement with asynchronous access, the college can bring expert instruction to every campus without waiting for large capital investments.This is precisely the sort of context-aware challenge that Smart India Hackathon teams can address through innovative but lightweight software solutions. Expected Solution: Student teams should outline a software-only virtual-classroom ecosystem that delivers clear audio and concise visual content on limited bandwidth, supports interactive live as well as recorded sessions, and allows easy content access for learners who may need to download materials during off-peak hours-all without relying on specialised hardware or costly licences.",
"ps_number": "SIH25101",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Remote classroom for rural colleges"
}
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"introduction": "Background: By the time term-end marks reveal failures, many struggling students have disengaged beyond recovery. Counsellors and mentors need a mechanism that surfaces risk indicators-faliing attendance, high number of attempts exhausted to pass a particular subject, reducing test scores-weeks earlier. Description: Attendance percentages live in one spreadsheet, test results in another, and fee-payment data in a third. No single view exists to signal that a learner is slipping in multiple areas simultaneously. Commercial analytics platforms promise predictive insights but demand funds and maintenance beyond the reach of public institutes. A simpler, transparent approach would merge existing spreadsheets,apply clear logic to colour-code risk,and notify mentors on a predictable schedule.Such a system must be easy to configure, require minimal training, and empower educators-not replace their judgment. By focusing on data fusion and timely alerts rather than complex algorithms, the institute can intervene early and reduce drop-out rates without fresh budget lines.This challenge epitomises the hackathon spirit: take what is already present, integrate it cleverly, and produce meaningful impact using machine learning. Expected SoIution: Participants are expected to build a consolidated digitaI dashboard that automatically ingests attendance,assessment scores, and other student related dala;applies clear, rule-based thresholds to identify at-risk students; highIights them in an intuitive visual format; and dispatches regular notifications to mentors and guardians, ensuring early,data-driven intervention achieved entirely through suitable machine learning approaches.",
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},
"organization": "Government of Rajasthan",
"problem_description": "Background: By the time term-end marks reveal failures, many struggling students have disengaged beyond recovery. Counsellors and mentors need a mechanism that surfaces risk indicators-faliing attendance, high number of attempts exhausted to pass a particular subject, reducing test scores-weeks earlier. Description: Attendance percentages live in one spreadsheet, test results in another, and fee-payment data in a third. No single view exists to signal that a learner is slipping in multiple areas simultaneously. Commercial analytics platforms promise predictive insights but demand funds and maintenance beyond the reach of public institutes. A simpler, transparent approach would merge existing spreadsheets,apply clear logic to colour-code risk,and notify mentors on a predictable schedule.Such a system must be easy to configure, require minimal training, and empower educators-not replace their judgment. By focusing on data fusion and timely alerts rather than complex algorithms, the institute can intervene early and reduce drop-out rates without fresh budget lines.This challenge epitomises the hackathon spirit: take what is already present, integrate it cleverly, and produce meaningful impact using machine learning. Expected SoIution: Participants are expected to build a consolidated digitaI dashboard that automatically ingests attendance,assessment scores, and other student related dala;applies clear, rule-based thresholds to identify at-risk students; highIights them in an intuitive visual format; and dispatches regular notifications to mentors and guardians, ensuring early,data-driven intervention achieved entirely through suitable machine learning approaches.",
"ps_number": "SIH25102",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "AI-based drop-out prediction and counseling system"
}
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"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "Background: Admissions, fee collection, hostel allocation, and examination records are maintained in separate ledgers. Students queue at multiple counters; staff re-enter identical data; and administrators lack a real-time institutional overview. Description: Comprehensive ERP suites can unify data but remain out of financial reach for many public colleges. Yet much of the required functionality-customised online forms, central data tables, automated receipts, real-time dashboards-already exists in ubiquitous cloud office suites. By intelligently connecting these services, an institution can create a single source of truth without large capital outlay. Essential components include streamlined admission intake, automated fee receipting, live hostel occupancy tracking, and summary dashboards for higher officials. Data security, role-based access and regular back-ups must be baked in from day one. Because staff is already familiar with basic spreadsheet and form tools, the learning curve remains shallow, ensuring broad adoption. Hackathon participants can demonstrate how thoughtful process mapping and lightweight scripting turn an assortment of familiar apps into a cohesive, low-cost ERP that any college can replicate. Expected Solution: Teams should outline an integrated workflow in which admission data flows seamlessly into a central student database, financial transactions automatically generate digital receipts, hostel and library records update the same database in real time, and summary dashboards present key metrics to administrators all delivered through interlinked, widely available software services rather than proprietary or hardware-intensive solutions.",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Rajasthan",
"problem_description": "Background: Admissions, fee collection, hostel allocation, and examination records are maintained in separate ledgers. Students queue at multiple counters; staff re-enter identical data; and administrators lack a real-time institutional overview. Description: Comprehensive ERP suites can unify data but remain out of financial reach for many public colleges. Yet much of the required functionality-customised online forms, central data tables, automated receipts, real-time dashboards-already exists in ubiquitous cloud office suites. By intelligently connecting these services, an institution can create a single source of truth without large capital outlay. Essential components include streamlined admission intake, automated fee receipting, live hostel occupancy tracking, and summary dashboards for higher officials. Data security, role-based access and regular back-ups must be baked in from day one. Because staff is already familiar with basic spreadsheet and form tools, the learning curve remains shallow, ensuring broad adoption. Hackathon participants can demonstrate how thoughtful process mapping and lightweight scripting turn an assortment of familiar apps into a cohesive, low-cost ERP that any college can replicate. Expected Solution: Teams should outline an integrated workflow in which admission data flows seamlessly into a central student database, financial transactions automatically generate digital receipts, hostel and library records update the same database in real time, and summary dashboards present key metrics to administrators all delivered through interlinked, widely available software services rather than proprietary or hardware-intensive solutions.",
"ps_number": "SIH25103",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Automation",
"title": "ERP-based Integrated Student Management system"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "Background: Campus offices answer hundreds of repetitive queries-fee deadlines, scholarship forms, timetable changes often from students more comfortable in Hindi or other regional languages. Long queues and communication gaps strain both staff and learners.. Description: While answers reside in circulars and PDFs, students prefer conversational guidance. A multilingual chatbot that understands Hindi, English, and an additional local language can deflect routine inquiries, freeing staff for complex tasks. Modern conversational-Al services offer drag-and-drop interfaces. Key requirements include accurate intent recognition, context management across multiple turns, fallback to human contact when necessary, and daily conversation logs for continuous improvement. Embedding the chatbot on the college website and popular messaging platforms maximises reach. Crucially, the solution must respect privacy, ensure response accuracy, and remain maintainable by student volunteers after the hackathon concludes. By focusing on content curation and user-centric design, participants can deliver high impact solution quickly. Expected Solution: Student innovators should develop a multilingual conversational assistant that ingests institutional FAQs, recognises and responds to student queries in at least five regional languages, maintains context across follow-up questions, logs interactions for review, and integrates seamlessly into the college's existing web and messaging channels-achieving equitable, round-the-clock information access in a user friendly manner.",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "Government of Rajasthan",
"problem_description": "Background: Campus offices answer hundreds of repetitive queries-fee deadlines, scholarship forms, timetable changes often from students more comfortable in Hindi or other regional languages. Long queues and communication gaps strain both staff and learners.. Description: While answers reside in circulars and PDFs, students prefer conversational guidance. A multilingual chatbot that understands Hindi, English, and an additional local language can deflect routine inquiries, freeing staff for complex tasks. Modern conversational-Al services offer drag-and-drop interfaces. Key requirements include accurate intent recognition, context management across multiple turns, fallback to human contact when necessary, and daily conversation logs for continuous improvement. Embedding the chatbot on the college website and popular messaging platforms maximises reach. Crucially, the solution must respect privacy, ensure response accuracy, and remain maintainable by student volunteers after the hackathon concludes. By focusing on content curation and user-centric design, participants can deliver high impact solution quickly. Expected Solution: Student innovators should develop a multilingual conversational assistant that ingests institutional FAQs, recognises and responds to student queries in at least five regional languages, maintains context across follow-up questions, logs interactions for review, and integrates seamlessly into the college's existing web and messaging channels-achieving equitable, round-the-clock information access in a user friendly manner.",
"ps_number": "SIH25104",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Smart Education",
"title": "Language Agnostic Chatbot"
}
|
{
"category": "Software",
"details": {
"ai-based_anomaly_detection": null,
"background": null,
"conclusion": null,
"data_privacy_&_security": null,
"deliverables": null,
"description": null,
"digital_tourist_id_generation_platform": null,
"eligibility": null,
"evaluation_criteria": null,
"expected_outcomes": null,
"expected_solution": null,
"impact": null,
"impact___why_this_problem_needs_to_be_solved": null,
"innovative_features": null,
"introduction": "Kolams (known by other narnes as muggu, rangoli and rangavalli) are significant cultural traditions of India, blending art, ingenuity, and culture.\nThe designs vary by region, and the designs consist of grids of dots, with symmetry, repetition, and spatial reasoning embedded in them.\nThe Kolam designs provide a fascinating area of study for their strong mathematical underpinnings.\nThe challenge is to develop computer programs (in any language, preferably Python) to identify the design principles behind the Kolam designs and recreate the kolams.",
"iot_integration_optional": null,
"key_features": null,
"key_performance_parameters": null,
"mobile_application_for_tourists": null,
"multilingual_support": null,
"objective": null,
"problem_description": null,
"relevant_stakeholders___beneficiaries": null,
"supporting_data": null,
"technical_scope": null,
"tourism_department_&_police_dashboard": null
},
"organization": "AICTE",
"problem_description": "Kolams (known by other narnes as muggu, rangoli and rangavalli) are significant cultural traditions of India, blending art, ingenuity, and culture.\nThe designs vary by region, and the designs consist of grids of dots, with symmetry, repetition, and spatial reasoning embedded in them.\nThe Kolam designs provide a fascinating area of study for their strong mathematical underpinnings.\nThe challenge is to develop computer programs (in any language, preferably Python) to identify the design principles behind the Kolam designs and recreate the kolams.",
"ps_number": "SIH12507",
"s_no": 0,
"submitted_ideas_count": 0,
"theme": "Heritage & Culture",
"title": "Develop computer programs (in any language preferably Python) to identify the design principles behind the Kolam designs and recreate the kolams"
}
|
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