Crop Recommendation System using hybrid of KNN and Random Forest Classifier

Machine learning and its rapid advancement have significantly improved the way we interact with computers. We can find applications of machine learning in almost every field, like the IT industry, medicine, agriculture, etc. The idea of imparting machine learning to agriculture rose decades ago, and, as a result, many improvements were made in the field of agriculture. Various models are developed to predict the crop and yield using machine learning algorithms like decision trees, but the main problem with using algorithms like decision trees is that they do not provide the desired accuracy, which may lead to incorrect predictions. This paper proposes a user-friendly crop recommendation and yield prediction system. The user provides the following as input: state name, district name, soil type, and season. To recommend the crop and predict the yield of the crop, a combination of K-nearest neighbor (KNN) and random forest (RM) is used. The K-nearest neighbor algorithm showed 98% accuracy, and the Random Forest algorithm showed 96% accuracy