Predicting Agricultural Success with Machine Learning

This report applies the KNN algorithm, a simple yet effective machine learning method, to predict agricultural success. Through historical agricultural data, weather information, soil conditions, and other related data, the KNN model forecasts crop yields with high precision. This forecast will serve as a guide for farmers and industries in the agriculture sector to take data driven decisions to efficiently manage resources and increase productivity. For this purpose, we use KNN algorithm, which is a very straight forward and effective machine learning algorithm for classification and regression studies, to establish a model for crop yield prediction under different environmental and agricultural conditions. The research demonstrates the application of machine learning mainly K-nearest neighbours in improving agricultural practice and ensuring food security.