Advancing Agricultural Sustainability: Enhancing Crop Yield Prediction Through Regression Modeling

Crop yield prediction is essential for advancing agricultural practices and ensuring global food security. This study aims to enhance crop yield forecasts by employing regression models. We highlight the significance of improving crop yield prediction for agricultural advancement. The methods incorporated Machine learning algorithms, particularly regression techniques, are utilized with an emphasis on thorough data preprocessing, including feature selection and engineering. Various regression models, such as linear regression, decision trees, and ensemble methods, are employed to forecast crop yields based on diverse input variables. The research demonstrates the efficacy of the methodology in accurately predicting crop yields across different crops and geographical regions. The outstanding accuracy demonstrated by the Random Forest Regressor model, achieving an impressive R-squared score of 98.5%, indicative of its proficiency in capturing the variability inherent in crop yield data. The model performance is evaluated using the R-squared score, providing a comprehensive assessment of predictive accuracy. Our findings underscore the importance of employing regression models for crop yield prediction and emphasize the value of data-driven approaches in agricultural research. The study’s findings contribute to sustainable farming practices and initiatives aimed at addressing global food security concerns by furnishing farmers and stakeholders with dependable tools for yield prediction.