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OPTIMIZING CROP YIELD FORECASTING USING HYBRID MACHINE LEARNING MODELS

  • Unique Paper ID: 181990
  • Volume: 12
  • Issue: 2
  • PageNo: 287-292
  • Abstract:
  • Crop yield prediction using machine learning enhances decision-making in agriculture by providing accurate and timely forecasts. This study utilized datasets including climate data, soil properties, satellite imagery, and historical yields. Four models were evaluated: Random Forest, XGBoost, Decision Tree, and Linear Regression. Random Forest Regression achieved the best performance with an R² of 0.97, MAE of 5,412, and RMSE of 12,450.XGBoost followed with an R² of 0.87, showing potential for improvement through hyperparameter tuning. Decision Tree Regression showed overfitting, with perfect training R² but a slightly lower test R² of 0.95.Linear Regression underperformed with an R² of 0.67, failing to capture complex patterns. Remote sensing and advanced analytics enhanced prediction accuracy and real-time monitoring. All models faced issues with infinite MAPE due to zero-yield values, highlighting the need for data cleaning. Random Forest proved to be the most reliable model, promoting efficient and sustainable agricultural practices.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 2
  • PageNo: 287-292

OPTIMIZING CROP YIELD FORECASTING USING HYBRID MACHINE LEARNING MODELS

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