Data analytics in ensemble learning for effective crop yield prediction

Abstract In agricultural research, Crop Yield Prediction (CYP) offers the best decision-making to assist farmers in agricultural yield forecasting efficiently. Most of the existing studies have not considered the exhaustive exploration of data analytics techniques in Machine Learning (ML) for CYP due to which the existing models have not given the optimal results. The main objective of this study is to investigate the effectiveness of data analytics in Ensemble Learning (EL) techniques for more reliable and high-performance CYP models. This article proposes an expert system model, Blended Expert System for Crop Yield Prediction (BESCYP), designed to predict the precise crop yields for specific agricultural lands in the Assam state of India. The proposed BESCYP employs Expectation–Maximization (EM) algorithm to treat missing values, the Isolation Forest (IF) technique to analyze outliers, the Genetic Algorithm (GA) to perform feature selection, Robust Scaling (RS) technique to perform data normalization and the Extra Tree (ET) for classification that overcome the variance and overfitting problem commonly associated with standard ML algorithms. The evaluation of the proposed BESCYP model has been performed using accuracy, precision, recall, and F-1 score on a dataset obtained from International Crops Research Institute for Semi-Arid Tropics (ICRISAT). The proposed model is compared against different standard ML algorithms, EL algorithms and various existing models available in the literature, and the experimental results show that the proposed BESCYP model outperforms other models.