Machine Learning Techniques for Weather based Crop Yield Prediction

India’s population is more than 50% depends on agriculture for existence, making it the foundation of the country’s economy. Variations in weather, temperature, and other environmental factors are now a significant threat to the continued success of agriculture. The decision support tool for Crop Yield Prediction (CYP), which includes assisting decisions on which crops to plant and what to do during the growth season of the crops, is where machine learning (ML) plays a vital role. The goal of the current study is the prediction of crop yield, crop recommendation to extract and synthesize the CYP traits. In addition, several methodologies have been created to examine agricultural yield prediction utilizing Machine learning techniques like gradient boosting, decision tree, and random forest algorithms are utilized to generate accurate forecasts and advise on the best crops.