Market Analysis for Farmers using Machine Learning

Abstract

Using big data Data mining and predictive analysis have developed to be helpful for examining crop prices in agriculture. This paper considers applying methods of Big Data and data mining utilising agricultural data. As a result of an outdated chain structure for selling the crops in their various markets, farmers are increasingly concerned with getting the full worth of the commodity. Due to the fact that farmers in the area may only sell their goods in a way that indirectly affects consumers, the market's true potential is still untapped. E-agriculture is a growing industry that focuses on improving farming and advancing provincial development through better data and correspondence forms. The market has a growing amount of data, just as other industries. Data from the various markets in the Bangalore area are taken into account and employed in this work's data mining decision tree models. Farmers are the backbone of our nation, which is dealing with a number of issues. Among these issues, getting pertinent information about the crop and weather specifics at the appropriate moment is one of the most significant. The primary objective of the Map-Reduce-based decision tree model is to give Farmers the greatest price for their product. In comparison to other models, the output of the proposed Map Reduce model is more accurate at predicting the best price from the existing market data.