KrishiNetra is an end-to-end MVP designed to empower farmers and agricultural stakeholders with data-driven insights. Built for the Maharashtra Agrihackathon 2026, it combines satellite imagery analysis with Generative AI to bridge the gap between complex data and actionable farming decisions.
- 🛰️ Satellite NDVI Analysis: Visualizes vegetation health using pre-computed NDVI overlays to identify high-stress zones.
- 📄 AI Document Extraction: Uses Google Gemini to automatically parse and verify complex PDF land records (7/12 extracts).
- 🌾 Crop Prediction: Context-aware crop identification based on geographic coordinates and local lookup data.
- 📍 Interactive Mapping: Built with Streamlit and Folium for a seamless, location-based user experience.
- 🛡️ Stress Detection: Automated calculation of "at-risk" farm areas to prioritize irrigation and intervention.
# Navigate to the MVP directory
cd krishinetra_mvp
# Activate venv (from repo root)
..\krishinetra_venv\Scripts\Activate.ps1
# Install deps
pip install -r requirements.txt
# Run app
streamlit run app.pykrishinetra_mvp/
├── app.py # Main app
├── modules/ # Python modules
│ ├── map_viz.py # Folium map
│ ├── ndvi_loader.py # NDVI PNG loader
│ ├── crop_predictor.py # Lookup crop classifier
│ ├── stress_detector.py # Threshold stress zones
│ ├── area_calculator.py # Shapely area math
│ └── doc_extractor.py # Gemini PDF extractor
├── data/
│ ├── ndvi_overlays/ # Pre-computed NDVI PNGs
│ ├── sample_documents/ # Demo land record PDF
│ └── crop_lookup.json # Coordinate→crop map
├── .env # Gemini API key (optional)
└── requirements.txt
For document AI, add your key to .env:
GEMINI_API_KEY=your_key_here
Without it, the app uses fallback demo data.
Select a demo scenario from the sidebar:
- A: Cotton Farm Stress - Vidarbha — NDVI stress analysis
- B: Onion Fields - Nashik — Mixed vegetation health
- C: Land Document Verification - Pune — AI document extraction