Published Paper Details:

The accurate prediction of crop is crucial for effective agricultural planning and resource mobilization. This project constructs and enhances the system of predicting crop yield harnessing the features of the agriculture environment. The important environmental features to be focused on includes among other soil type, rainfall and temperature by implementing various feature selection methods including BORUTA and Recursive Feature Elimination (RFE). These features are fed to the ensemble of machine learning algorithms such as Random Forest, SVM and KNN to improve the prediction accuracy. The system also provides fertilizer application and yield prediction. Testing in databases yields promising results in the improvement of precision and decision making. The proposed model proves to be reliable and cost efficient and assists farmers with actionable information to enhance crop productivity and sustainable farming techniques.