A Robust Crop Yield Forecasting Model Through Machine Learning Ensemble Techniques

Accurate crop yield forecasting plays a vital role in effective agricultural management and resource planning. This study presents an advanced machine learning approach that utilizes comprehensive data and rigorous preprocessing to enhance model accuracy. A range of core regression techniques, including Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), XGBoost Regressor (XGBR), Decision Tree Regressor (DTR), CatBoost, and Extra Trees Regressor (ETR), were applied as foundational models for yield estimation. To further optimize predictions, ensemble methods such as Voting Regressor, Stacking Regressor, Bayesian Model Averaging (BMA), and Blending Ensemble were integrated, leveraging the strengths of individual models to achieve robust performance. This approach highlighted the effectiveness of ensemble frameworks in yield forecasting. The findings offer valuable insights for informed decision-making in agriculture, underscoring the methodology's potential for supporting policy development and improving predictive accuracy in agricultural management.