Machine Learning Techniques for Crop Yield Prediction

A Survey on Techniques, Applications, and Future Directions

Abstract

Accurate crop yield prediction is crucial for global food security and effective agricultural management. This paper reviews the latest machine learning (ML) techniques used in crop yield prediction, focusing on applications, challenges, and future prospects. Traditional methods based on historical data often fail to capture the complexities of environmental and management factors. Recent ML advancements have improved accuracy by using high-dimensional data like satellite imagery and weather patterns. Key challenges include data quality, model interpretability, generalizability, and computational demands. Solutions such as data sharing, interpretable models, transfer learning, and hybrid approaches are proposed to advance crop yield prediction and promote sustainable agriculture.