Momahmoses/crop-yield-forecasting

Satellite NDVI combined with LightGBM for crop yield prediction in North-Central Nigeria with a Streamlit dashboard. # Agricultural Crop Yield Forecasting & Farmland Monitoring Satellite-imagery and ML-powered yield prediction platform for smallholder farmers across North-Central Nigeria, integrating NDVI time-series, weather, soil properties, and farm management data to forecast crop yields per hectare per growing season. --- ## Problem Statement Smallholder farmers in Nigeria's food belt lack access to yield forecasts, making it impossible to plan inputs, storage, or marketing. This platform provides per-farm, per-season yield forecasts to improve food security and income planning. --- ## Features | Feature | Description | |---------|-------------| | Multi-Crop Support | Maize, Sorghum, Millet, Cassava, Yam, Rice, Groundnut | | NDVI / EVI Integration | Sentinel-2 and Landsat-8 spectral indices | | LightGBM Regression | Yield prediction with feature importance ranking | | Leave-One-Season-Out CV | Robust temporal generalisation testing | | Streamlit Dashboard | Yield map, season trends, custom forecasting tool | --- ## Input Features | Feature | Source | |---------|--------| | `ndvi_mean` | Sentinel-2 / Landsat-8 | | `rainfall_mm` | NIMET rainfall stations | | `soil_quality` | FAO HarvestChoice soil data | | `temperature_mean` | ERA5 reanalysis | | `farm_management` | Extension officer surveys | | `crop_type` | Satellite classification | --- ## Tech Stack | Layer | Technology | |-------|-----------| | Remote Sensing | NDVI/EVI from Sentinel-2, Landsat-8 | | Machine Learning | LightGBM, scikit-learn | | Geospatial | GeoPandas, Folium | | Dashboard | Streamlit, Plotly | | Data | pandas, NumPy | --- ## Quick Start ```bash git clone github.com cd crop-yield-forecasting pip install -r requirements.txt streamlit run streamlit_app.py ``` --- ## Author **Momah Moses**, Geospatial AI Engineer & Data Scientist GitHub · Portfolio