Predicted crop yield using satellite imagery (Sentinel-2) and ground truth data. Applied classical ML models (RF, XGBoost, LightGBM) and deep learning models (LSTM, GRU) with transfer learning.
- Year
- 2025
- Status
- Completed
- Stack
- Python, Google Earth Engine, LightGBM, LSTM, Sentinel-2
- Source
- GitHub
What I built
- Predicted crop yield using satellite imagery (Sentinel-2) and ground truth data.
- Collected and preprocessed data from East and West Java (2020-2021).
- Applied classical ML models (RF, XGBoost, LightGBM) and deep learning models (LSTM, GRU).
- Implemented transfer learning for the Lampung region to improve prediction accuracy.
- Achieved region-specific yield predictions with improved accuracy.
- Gained skills in integrating geospatial data with ML/DL models and remote sensing analysis.