Comparative Analysis of Machine Learning Approaches in Smart Agriculture

In India's economy, agriculture is crucially significant. Agriculture automation is a major source of concern and a hot topic all around the world. The world's population is constantly growing, and with it comes increased demand for food as well as occupation. The farmers' old methods were inadequate to satisfy these requirements. As a result, new automated approaches were proposed. These new methods met food demands while simultaneously providing work opportunities for billions of people. Several methods are used for efficient farm management like IoT, cloud computing, AI, machine learning, deep learning, big data, etc. Among these big data is an emerging research area for crop yield prediction. Traditionally, these forecasts were reliant on the farmers' expertise, but now they can roughly anticipate the crop output on their farm by employing a variety of methods. In this paper, we have proposed a recommendation model along with an algorithm to evaluate the future year's crop production. We have compared the random forest machine learning algorithm with the big data algorithm. A brief comparison has been made with these algorithms. The proposed model has been implemented using python.