Ensemble-Driven Machine Learning Regression Models for Climate-Sensitive Crop Yield Prediction: A Comparative Performance Analysis

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 1

Year of Publication : 2026

Authors : Siva Subramanian R, M Elumalai, B.Saratha, K.Ramesh, K.Sudha, J.Gnana Jeslin

10.14445/23488549/IJECE-V13I1P114

How to Cite?

Siva Subramanian R, M Elumalai, B.Saratha, K.Ramesh, K.Sudha, J.Gnana Jeslin, "Ensemble-Driven Machine Learning Regression Models for Climate-Sensitive Crop Yield Prediction: A Comparative Performance Analysis," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 1, pp. 157-173, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I1P114

Abstract:

Precise forecasting of crop yields is the key to food security, resource management, and sustainable food farming. This paper will examine how different Machine Learning (ML) models can be used to predict crop yield in relation to climatic and other environmental conditions, like rainfall, temperature, and the use of pesticides. Multiple performance metrics, such as R², Mean Squared Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to train and evaluate seven ML models which were Linear Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Gradient Boosting (GB), XGBoost, Random Forest (RF), and Bagging. The experimental findings showed that the ensemble-based models were very effective compared to the traditional regression and distance-based algorithms. The Bagging recorded the best prediction accuracy in terms of R² score, closely followed by the RF. The two models were effective in capturing nonlinear relationships and high generalization in varied climatic and crop conditions. On the other hand, the simplicity of models like LR and KNN demonstrated low predictive abilities. The results highlight the scalability and the strength of the Ensemble Learning(EL) techniques in crop yield forecasting. The paper concludes with a set of recommendations on how to incorporate Explainable AI, real-time data that uses IoT, and region-specific hybrid deep learning systems to improve the interpretability, adjustment, and accuracy of agricultural forecasting systems in the future.

Keywords:

Bagging, Crop Yield Prediction (CYP), Ensemble Learning, Machine Learning, Random Forest.

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