aaombase/AgroSphere

AI-driven crop yield and market intelligence system using NDVI satellite data, weather, soil and mandi trends. Includes ML-based yield prediction, price forecasting, and a FastAPI backend with Docker and multilingual RAG insights. # AgroSphere AgroSphere is a smart farming platform that combines a Node.js backend, multiple Python AI services, IoT integrations, and a mobile app into one agriculture intelligence stack. ## Repository Overview - `backend/`: Main Node.js API with auth, farm data, alerts, digital twin, and AI orchestration - `api/`: FastAPI service for commodity price forecasting - `ai_service/`: Price forecasting, NDVI, RAG, and selling-intelligence services - `climate-resilience-service/`: Climate resilience scoring microservice - `demand_supply_service/`: Demand and supply forecasting service - `plant-disease-service/`: Plant disease detection service - `yield-engine/`: Yield prediction services and datasets - `Iot hardware/`: Raspberry Pi and field hardware integration assets - `Mobile/`: Expo-based mobile application ## Quick Start ### Prerequisites - Node.js 14+ - Python 3.8+ - MongoDB (recommended for the backend) ### Setup 1. Copy `.env.example` to `.env` and fill in the required API keys. 2. Install Node.js dependencies where needed. 3. Create Python virtual environments: ```powershell npm run setup:venv ``` 4. Start the full stack orchestrator: ```powershell npm run dev ``` 5. Check service health: ```powershell npm run health ``` ## Core Endpoints - Backend API: `localhost` - Unified Gateway: `localhost` - Climate Resilience Service: `localhost` ## Helpful Docs - QUICK_START_GUIDE.md - ENV_SETUP.md - README_DOCKER.md - backend/README.md - api/README.md - ai_service/README.md - climate-resilience-service/README.md - plant-disease-service/README.md - yield-engine/README.md ## Notes - Service-specific setup details live inside each service directory. - Keep `.env` files and trained model artifacts out of version control.