A precision agriculture platform leveraging satellite imagery and AI to provide real-time crop health monitoring, intelligent diagnostics, and actionable farming insights.
- Satellite Analysis: Automated processing of multispectral satellite imagery with NDVI, EVI, and SAVI vegetation indices
- Health Monitoring: Real-time crop health assessment with zone-based segmentation and anomaly detection
- AI Assistant: Intelligent chatbot providing crop-specific recommendations and problem diagnosis
- Weather Integration: Current conditions and forecasts correlated with crop health metrics
- Farm Management: Multi-farm dashboard with historical trend analysis and performance tracking
Frontend
- Next.js 16 with React 19
- TypeScript
- Tailwind CSS v4
- Supabase Auth
- React Query for state management
- Recharts for data visualization
- React Leaflet for mapping
Backend
- FastAPI (Python)
- Supabase (PostgreSQL)
- Google Gemini AI
- OpenWeather API
- NumPy for spectral analysis
- Node.js 20+
- Python 3.12+
- Supabase account
- Google AI API key
- OpenWeather API key
cd backend
pip install -r requirements.txt
cp .env.example .env
# Add your API keys to .env
python main.pycd frontend/agrovision-app
npm install
cp .env.local.example .env.local
# Add your Supabase credentials to .env.local
npm run devdocker compose up --buildBackend (.env)
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_service_key
GOOGLE_API_KEY=your_gemini_api_key
OPENWEATHER_API_KEY=your_openweather_key
FRONTEND_URL=http://localhost:3001
Frontend (.env.local)
NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key
NEXT_PUBLIC_API_BASE_URL=http://localhost:8000
Run the SQL schema:
psql -h your_supabase_host -U postgres -d postgres -f backend/supabase_schema.sqlOnce the backend is running, visit http://localhost:8000/docs for interactive API documentation.
MIT