Crop Recommendation System Using Machine Learning Algorithms

Unquestionably, agriculture provides the primary source of income in rural India, along with related enterprises. The country’s Gross Domestic Product is significantly impacted by the agriculture industry (GDP). The size of the agricultural industry is excellent for the country. However, the crop production per hectare falls short of world norms. This is one of the global standards. The higher suicide rate among marginal farmers in India is most likely due to this. This study provides a useful and understandable yield prediction technique for farmers. Farmers can access the internet through a smartphone application that has been developed. Locating users is made easier with GPS. User enters location. The most profitable crop list can be selected using machine learning algorithms, and they can also forecast crop yields for user-selected crops. Selected machine learning techniques, including Support vector machines (SVMs), artificial neural networks (ANNs), random forests (RFs), multivariate linear regression (MLR), and K-Nearest Neighbour (KNN), are employed to forecast crop productivity. Random forest outperformed the others with an accuracy rate of 95% The analysis also recommends the ideal fertiliser application window to increase productivity.