Enhancing Crop Yield Prediction: Leveraging Advanced Machine Learning Models for Improved Accuracy

Abstract

The accurate prediction of crop yields is critical for food security and agricultural planning. Traditional methods often fall short in addressing the complex, non-linear relationships between various factors influencing crop yields. This paper explores how advanced machine learning models can enhance crop yield prediction accuracy. By leveraging a range of techniques, including regression models, decision trees, and ensemble methods, we propose a framework that integrates multiple data sources for robust predictions. We evaluate the effectiveness of these models using historical crop data and present a comparative analysis to highlight their potential benefits.