FarmEasy: An Intelligent Platform to Empower Crops Prediction and Crops Marketing

Farmers' contribution to the economy and national GDP is ineffable. Even though there has been a significant technological advancement in the field of agricultural crops production and management, farmers in developing countries still follow the traditional methods of farming which at many times leads them a loss. Moreover, they don't know the correct market value of their crops, and distributors befool them with the price and value. On the other hand, in times of price hikes and crisis, due to the proper channel government failed to buy crops from them. This paper aims to analyze the primitive approach of cultivation and develop a model for crop prediction using machine learning and provide a model for proper crops management after production. We propose an intelligent system that can predict the best possible crops only by providing the present location of a farmer, the overall guideline from soil preparation to crop yielding, and the systemic approach of crops marketing from farmer to consumer. We used Random Forest Regression, Support Vector Regression and Voting Regression techniques for crop yield prediction and used the real-time data of climate, weather, and soil for the specific region. On the other hand, the market monitoring system will help for proper pricing of the crops and provide transparency for all the stakeholders related to crop marketing where they can buy and sell their products utilizing our system.