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Crop Yield Prediction And Crop Recommendation As Plays A Crucial Role In Agricultural Decision-making Processes, Enabling Farmers To Optimize Resource Allocation And Plan For Potential Risks. In Recent Years, Machine Learning Algorithms Have Emerged As Powerful Tools For Predicting Crop Yields Accurately. This Abstract Focuses On The Application Of The Decision Tree Algorithm To Train For Crop Yield Prediction. Once The Decision Tree Model Is Constructed, It Can Be Used To Predict Crop Yields For Unseen Data. New Input Variables, Such As Weather Forecasts Or Soil Measurements, Can Be Fed Into The Model To Obtain Yield Predictions And Crop Recommend. The Interpretability Of Decision Trees Allows Farmers To Understand Which Factors Contribute Most Significantly To Crop Yield Variations And Make Informed Decisions Accordingly. User To Give Details Then Model Is Predicted Crop .

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