Intelligent Crop Recommendation using Machine Learning

Agriculture is a vital domain for the economic development of many countries. For farmers, it is essential to choose an appropriate crop to plant depending on a variety of environmental parameters. In order to predict the best crop for cultivation considering the soil quality, climate, and other criteria, this article provides a system for crop recommendations employing machine learning, especially the Random Forest algorithm (RF). The methodology involved collecting data from various sources, pre-processing the data, selecting relevant features, training and testing the model, and evaluating the model's performance. The results showed that the system is able to predict the best crop to grow with an accuracy of 99%. The system can help farmers make data-driven decisions, maximize their yields, minimize risks, and reduce their environmental impact. This technology has the potential to revolutionize the agriculture industry and improve the livelihoods of farmers worldwide. In addition to providing crop recommendations, the system also offers a user-friendly interface that displays recommendations in a clear and understandable way. This makes the system accessible to farmers with varying levels of technical expertise.