A Machine Learning Approach for Crop Prediction and Crop Yield Prediction

Abstract

Agriculture is the major source of food supply for the man-kind. In the light of decreasing crop production currently, deciding the right crop for the harvestable land has become the crucial criteria of agriculture. Therefore, we have pro-posed a method which helps in suggesting the most suitable crop for a specific land and predict its yield based on the analysis of geographical and climatic parameters using ma-chine learning techniques. In our project, we have imple-mented Random Forest regressor, decision tree regressor and gradient boosting regressor algorithms for crop yield prediction model and random forest classifier and decision tree classifier for crop prediction model. Algorithms were trained with proper training data set initially and later test-ed with test dataset. Then all the algorithms were compared based on certain metrics and the best algorithms were cho-sen for further implementation.