gayathry32/Cocoa-Yield-Prediction-in-Ghana-using-Remote-Sensing-Random-Forest-2000-2023

Cocoa yield prediction for Ghana (2000–2023) using FAOSTAT data with CHIRPS rainfall, ERA5 temperature, and MODIS NDVI. Features aligned to cocoa phenology and modeled via Random Forest with time-based validation (train: 2000–2018, test: 2019–2023). Achieved ~62% R², supporting early warning and trading signal applications. # 🌍 Cocoa Yield Prediction – Ghana (2000–2023) ## 📌 Project Overview This project develops a **cocoa yield prediction model for Ghana (2000–2023)** by integrating **FAOSTAT yield data** with seasonal **satellite and climate indicators**: - 🌧️ **CHIRPS Rainfall** (May–Aug, flowering & pod set) - 🌡️ **ERA5-Land Temperature** (May–Aug, heat stress) - 🌱 **MODIS NDVI** (Aug–Oct, pod filling canopy vigor) Features are aggregated according to cocoa phenology and joined with yearly FAOSTAT yield statistics. A **Random Forest regression** model was trained and validated using a **time-based split**: - **Train:** 2000–2018 - **Test:** 2019–2023 --- ## ⚙️ Methodology 1. **Labels (FAOSTAT)** - Download production (tonnes) & harvested area (ha). - Compute yield = Production / Area. 2. **Seasonal Predictors** - Aggregate CHIRPS rainfall (May–Aug sum). - Aggregate ERA5 temperature (May–Aug mean, °C). - Aggregate MODIS NDVI (Aug–Oct mean, scaled 0–1). 3. **Preprocessing** - Reduce to national averages for Ghana. - Join yearly features with yield labels. 4. **Model Training & Validation** - Train Random Forest regression (500 trees). - Evaluate on test years (2019–2023). --- ## 📊 Results - **R² (Test):** ~0.62 - **RMSE / MAE:** Reasonable error margins on test years. - **Directional Hit-Rate:** Correctly predicted yield trend (up/down) in most years. - **Feature Importance:** Rainfall > NDVI > Temperature. **Charts produced:** - Time series: Observed vs Predicted yield (2019–2023). - Scatter plot: Observed vs Predicted yields with regression line. --- ## 🚀 How to Run ### A) Run in Google Earth Engine (recommended) 1. Upload FAOSTAT Ghana cocoa CSV as a **Table asset**. 2. Open `ghana_cocoa_yield_gee.js` in GEE Code Editor. 3. Replace `FAOSTAT_ASSET` with your asset ID. 4. Click **Run** → view metrics & charts in Console. 5. In **Tasks**, export predictions CSV to Google Drive. ### B) Optional: Python Post-Processing - Place exported CSV in `data/`. - Install requir …