Abstract

The crop recommendation system employing machine learning methods will be covered in this study. For sustainable agricultural practices to be followed and to increase crop yields, crop advice is crucial. Based on several factors, including nitrogen (N), phosphorus (P), potassium (K), and humidity, we will advise the best crop for the given site. We analyzed various algorithms like KNN, Decision Tree, Random Forest, SVM etc. But based on various accuracy levels we committed to random forest implementation. Means in this paper we are going to implement a crop recommendation system using random forest algorithm. The model is allowed to train upon a large dataset and the Performance of the recommendation system is measured using accuracy score. Finally, Using the trained model we are going to predict suitable crops for land according to the given parameters. Our proposed approach can be helpful for farmers, researchers, and policymakers in making informed decisions regarding crop management and planning.