Using A Two-Fold Machine Learning Approach for Crop And Fertilizer Recommendation System

A farmer's poor decision may have a significant negative impact on his family, his finances, and sometimes even his life. It also wastes a lot of land and productivity. This system encourages farmers to cultivate their land in an efficient way so that it gives the better production. We are putting out a system that is mostly based on variables like temperature, precipitation, market research, and water availability, among others. In the beginning, the environmental data that is collected from farmers or agriculture department offices is pre-processed in Python environment which is analyzed and visualized. Second, various machine learning algorithms are used to gain the results of prediction and accuracy of the data that will be comprehended and useful to farmers for decision-making and keeping track of their fields. Furthermore, the harvests have been predicted and displayed using a self-created recommender system. Farmers are expected to employ these technologies to boost their produce by making wiser field decisions.