Stacked ensemble model for accurate crop yield prediction using machine learning techniques

Abstract Predicting crop yields remains a crucial challenge in agriculture, as these forecasts influence decision-making at global, regional, and individual crop levels. Historically, such predictions have utilized diverse data sources, including agricultural, land, climatic, atmospheric, and other pertinent information. Over the past several years, the application of machine learning techniques has emerged as a valuable analytical approach for estimating agricultural productivity, thereby informing decisions regarding crop selection and management strategies throughout the entire growing cycle. Various kinds of machine learning models have been utilized in research to forecast crop yields. Our work proposes a stacked ensemble model designed for the purpose of predicting crop yield. The proposed model employs a stacked ensemble learning approach, with a Decision Tree Regressor functioning as the meta-model to amalgamate predictions from six distinct base learner models: Linear Regression (LR), Elastic Net, XGBoost Regressor, K-Neighbors Regressor (KNR), AdaBoost Regressor, and Random Forest Regressor (RFR). The proposed stacked ensemble model achieves superior crop yield prediction performance, evidenced by a notable enhancement in accuracy and a significant decrease in RMSE, surpassing the predictive capabilities of traditional machine learning models. The ensemble model’s performance was assessed using several metrics, including a Mean Absolute Error of 7.20 tons/hectare, Mean Square Error of 15570.32 tons 2 /hectare 2 , Root Mean Square Error of 124.78 tons/hectare, and Coefficient of Determination (R 2 Score) of 0.98. The performance results demonstrate that stacked ensemble model outperforms other conventional machine learning approaches, achieving a high R-squared score of 98%.