Crop Recommendation System to Maximize Crop Yield in Ramtek region using Machine Learning

Abstract

In Indian economy and employment agriculture plays major role. The most common problem faced by the Indian farmers is they do not opt crop based on the necessity of soil, as a result they face serious setback in productivity. This problem can be addressed through precision agriculture. This method takes three parameters into consideration, viz: soil characteristics, soil types and crop yield data collection based on these parameters suggesting the farmer suitable crop to be cultivated. Precision agriculture helps in reduction of non suitable crop which indeed increases productivity, apart from the following advantages like efficacy in input as well as output and better decision making for farming. This method gives solutions like proposing a recommendation system through an ensemble model with majority voting techniques using random tree, CHAID, K _ Nearest Neighbour and Naive Bayes as learner to recommend suitable crop based on soil parameters with high specific accuracy and efficiency. The classified image generated by these techniques consists of ground truth statistical data and parameters of it are weather, crop yield, state and district wise crops to predict the yield of a particular crop under particular weather condition.