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This study explores the use of machine learning techniques for predicting agricultural crop yields in India, focusing on maize, potatoes, rice, and wheat. By analyzing various factors such as weather conditions and pesticide usage, the researchers developed and compared multiple machine learning models, ultimately finding that the Decision Tree Regressor provided the highest accuracy with an R^2 score of 96%. The findings emphasize the importance of accurate crop yield predictions for enhancing food securit

Author

Rahul Kumar

Language

EN