| # 🌾 AgriEdge: Smart Farm Assistant | |
| An AI-powered assistant that uses real-time sensor data and textbook-based agricultural knowledge to provide insights, analysis, and actionable suggestions for small to medium-scale farms. Comes with both command-line and web interfaces. | |
| --- | |
| ## 🚀 Features | |
| - 📡 Analyzes **real-time farm sensor data** (soil, water, environment) | |
| - 📚 Retrieves context from **agricultural PDF documents** | |
| - 🤖 Uses **retrieval-augmented generation (RAG)** for grounded reasoning | |
| - 🧠 Powered by **Ollama + LLaMA 3** | |
| - 📝 Generates **natural language summaries and actionable insights** | |
| - 🔒 Runs **fully local** — no cloud, no data sharing | |
| - 🌐 Supports **Streamlit-based dashboard** for non-technical users | |
| --- | |
| ## 📁 Project Structure | |
| ```bash | |
| smartfarm/ | |
| ├── main.py # Command-line interface | |
| ├── app.py # Streamlit web app | |
| ├── llm/ | |
| │ ├── ollama_llm.py # Query handler using LLM + sensor data + RAG | |
| │ └── rag_pipeline.py # PDF retrieval pipeline using FAISS | |
| ├── logger.py # Logging setup | |
| ├── prompt.txt # Prompt template for LLM | |
| ├── data/ | |
| │ ├── farm_data_log.json # JSON file logging sensor readings | |
| │ ├── docs/ # Agricultural PDFs for knowledge retrieval | |
| │ └── faiss_index/ # Auto-generated FAISS vector index | |
| ``` | |
| --- | |
| ## 🛠️ Installation & Setup | |
| ### 1. Clone the Repository | |
| ```bash | |
| git clone https://github.com/your-username/smartfarm.git | |
| cd smartfarm | |
| ``` | |
| ### 2. Install Python Dependencies | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### 3. Set Up Ollama | |
| Make sure you have Ollama installed and running. | |
| Download the LLaMA 3 model: | |
| ```bash | |
| ollama run llama3 | |
| ``` | |
| Make sure Ollama is running in the background before using the assistant. | |
| ### 4. Add Sensor Data | |
| Append new entries to `data/farm_data_log.json`. Example format: | |
| ```json | |
| { | |
| "timestamp": "2025-07-22T21:00:00+01:00", | |
| "soil": {"moisture": "High", "pH": 6.8, "temperature": 24.9}, | |
| "water": {"pH": 7.2, "turbidity": "8 NTU", "temperature": 23.3}, | |
| "environment": {"humidity": "85%", "temperature": 26.0, "rainfall": "Moderate"} | |
| } | |
| ``` | |
| ### 5. Add Agricultural Documents (Optional) | |
| Place your farming-related PDFs inside: | |
| ```bash | |
| data/docs/ | |
| ``` | |
| The system will automatically build a searchable vector index. | |
| --- | |
| ## ▶️ Usage | |
| ### 📟 Command-Line Mode | |
| ```bash | |
| python main.py | |
| ``` | |
| You’ll be prompted to enter queries like: | |
| ```markdown | |
| > Is the soil suitable for planting now? | |
| > Has the turbidity improved compared to earlier? | |
| ``` | |
| Type `exit` to quit. | |
| ### 🌐 Streamlit Web Interface | |
| Launch the UI with: | |
| ```bash | |
| streamlit run app.py | |
| ``` | |
| What you can do: | |
| - View the most recent sensor snapshot | |
| - Ask farm-related questions like: | |
| - "What is the current soil condition?" | |
| - "Is it safe to irrigate now?" | |
| - "Has rainfall increased compared to earlier?" | |
| --- | |
| ## 💡 Notes | |
| - The system analyzes only the most recent sensor reading but uses the previous 2 for historical comparison (internally). | |
| - No internet connection is required once the vector store and model are set up. | |
| - Logs are written automatically to `logs/`. | |
| --- | |
| ## 🧪 Example Questions | |
| - "Is the soil moisture improving?" | |
| - "What is the overall environmental condition right now?" | |
| - "Is the water quality good for irrigation?" | |
| --- | |
| ## 📄 License | |
| MIT License | |