Yield Forecasting
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.
completed
public
Project Highlights
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.
Tech Stack
PythonGoogle Earth EngineLightGBMLSTMSentinel-2