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An efficient analysis of crop yield prediction using Hadoop framework based on random forest approach

In the growth of Information T echnology, Big data come forth as a blazing topic. The main source of human survival depends on agriculture; where it needs a key contribution in the field of crop data analysis. This paper gives a purpose about how to find experiences from accuracy agriculture information through big data approach. In this way, gathering the valuable data in an effective way drives a framework towards major computational challenges in crop analysis where information is remotely gathered. For the storage purpose of huge data availability in agriculture, we are intending Hadoop framework for our work to store a huge volume of crop data. This work gives a better prediction for the farmers to plant which kind of crops to their farm field based on their soil content to improve the productivity. The random forest algorithm is integrated with the MapReduce programming model in Hadoop framework.