Multi-Model Approaches to Crop Yield Prediction: Evaluating the Efficacy of Ensemble Algorithms

This study evaluates the efficacy of ensemble algorithms in crop yield prediction by integrating multiple predictive models. Recognizing the complexity and variability of agricultural data, the research employs a multi-model approach that combines different machine learning techniques to improve accuracy and robustness. The paper begins with a comparative analysis of individual models such as Random Forest, Support Vector Machine, and Neural Networks. It then explores various ensemble techniques, including Bagging, Boosting, and Stacking, to determine their effectiveness in enhancing prediction performance. Using a dataset of climatic, soil, and crop management variables from multiple regions, the models are trained and validated. The results indicate that ensemble methods, particularly Stacking, significantly outperform single-model predictions, providing more reliable and precise yield forecasts. This study contributes to agricultural informatics by demonstrating the potential of ensemble algorithms to address the challenges of crop yield prediction, offering valuable insights for researchers and practitioners aiming to optimize agricultural productivity.