The present study investigates the implementation of machine learning models on crop data to predict crop yield in Rajasthan state, India. The key objective of the study is to identify which machine learning model performs are better to provide the most accurate predictions. For this purpose, two machine learning models (decision tree and random forest regression) were implemented, and gradient boosting regression was used as an optimization algorithm. The result clarifies that using gradient boosting regression can reduce the yield prediction mean square error to 6%. Additionally, for the present data set, random forest regression performed better than other models. We reported the machine learning model's performance using Mean Squared Error, Mean Absolute Error and R-squared and identified that after the inclusion of gradient boosting regression, the accuracy increased to 92.77%. The MAE value decreased from 26.20 Mg/ha to 21.58 Mg/ha. The results indicate that machine learning models can improve the prediction of crop yield.
