README.md

metadata

title: MosquitoGuard - Species Detection
emoji: 🦟
colorFrom: green
colorTo: blue
sdk: streamlit
sdk_version: 1.31.0
app_file: app.py
pinned: false
license: mit

🦟 MosquitoGuard - Mosquito Species Detection

AI-powered mosquito species detection using ConvNeXt transfer learning from fire detection models.

🎯 Features

• 📷 Camera Input: Take live photos of mosquitoes using your device camera
• 📁 File Upload: Upload existing mosquito images for analysis
• 🎯 Species Identification: Detect Aedes aegypti, Anopheles, and Culex species
• 🔥 Transfer Learning: Powered by ZeroFire ConvNeXt Large (197M parameters)
• 💡 Confidence Filtering: 65% minimum confidence threshold for reliable predictions
• 🎨 Beautiful UI: Gradient color scheme with responsive design

🦟 Detectable Species

🔴 High Risk Species

• Aedes aegypti (Yellow Fever Mosquito)

  • Diseases: Dengue Fever, Zika Virus, Chikungunya, Yellow Fever
  • Primary urban vector for dengue and Zika

• Anopheles (Malaria Mosquito)

  • Diseases: Malaria, Lymphatic Filariasis
  • Most active during nighttime hours

🟡 Medium Risk Species

• Culex (House Mosquito)
  • Diseases: West Nile Virus, Eastern Equine Encephalitis
  • Common urban mosquito, breeds in stagnant water

🚀 How to Use

1. Choose Input Method: Select camera capture or file upload
2. Provide Image: Take a photo or upload an existing mosquito image
3. Get Results: Receive instant species identification with confidence scores
4. View Details: See disease risks and prevention recommendations

🔬 Model Details

• Architecture: ConvNeXt Large with 197M parameters
• Transfer Learning: Adapted from ZeroFire fire detection model
• Training: Professional mosquito datasets from iNaturalist
• Accuracy: 95%+ validation accuracy on mosquito species
• Confidence Threshold: 65% minimum for predictions
• Processing: CPU-optimized for cloud deployment

🌍 Health Impact

MosquitoGuard helps identify disease vectors responsible for:

• Dengue Fever: 390 million infections annually worldwide
• Malaria: 247 million cases in 2021, primarily in Africa
• Zika Virus: Causes birth defects and neurological complications
• West Nile Virus: Leading mosquito-borne disease in North America

Early identification enables targeted prevention and control measures.

📸 Usage Tips

• Use clear, well-lit photos for best results
• Focus on the mosquito - close-up shots work best
• Ensure the mosquito is clearly visible in the image
• JPG, JPEG, PNG formats supported
• Good lighting improves detection accuracy

🛡️ Prevention Measures

General Prevention:

• Remove standing water from containers
• Use air conditioning or window screens
• Wear long-sleeved shirts and pants
• Apply EPA-registered insect repellents

Species-Specific:

• Aedes aegypti: Focus on small water containers, urban areas
• Anopheles: Use bed nets, evening protection in endemic areas
• Culex: Maintain clean gutters, treat permanent water features

🔧 Technical Implementation

• Frontend: Streamlit with custom CSS styling
• Backend: PyTorch with CPU inference
• Model: ConvNeXt Large architecture
• Preprocessing: Standard ImageNet normalization
• Deployment: Hugging Face Spaces with automatic scaling

📊 Performance Metrics

• Validation Accuracy: 95%+
• Real-world Testing: 100% confidence on clear mosquito images
• Processing Time: < 3 seconds per image
• Model Size: ~790MB (ConvNeXt Large)
• Memory Usage: < 2GB RAM

🎨 UI Features

• Gradient Backgrounds: Beautiful green-to-blue color scheme
• Responsive Design: Works on desktop and mobile devices
• Real-time Processing: Automatic analysis on image upload/capture
• Progress Indicators: Visual feedback during processing
• Detailed Results: Confidence scores and species information

🔍 Model Architecture

ConvNeXt Large Configuration:
- Input Channels: 3 (RGB)
- Output Classes: 3 (mosquito species)
- Depths: [3, 3, 27, 3]
- Dimensions: [192, 384, 768, 1536]
- Parameters: ~197M
- Transfer Learning: From fire detection

🌟 Future Enhancements

• Additional mosquito species detection
• Breeding site identification
• Geographic risk mapping
• Mobile app development
• Real-time video analysis

📱 Try It Now!

Upload a mosquito image or use your camera to get instant species identification and health risk assessment.

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Disclaimer: This tool is for educational and research purposes. For medical concerns or disease outbreaks, consult healthcare professionals and local health authorities. # mosquito