Evaluation of predictive data mining algorithms in soil data classification for optimized crop recommendation

Abstract

Agricultural research has strengthened the optimized economical profit, internationally and is very vast and important field to gain more benefits. However, it can be enhanced by the use of different technological resources, tool, and procedures. Today, the term data mining [1][2] is an interdisciplinary process of analyzing, processing and evaluating the real-world datasets and prediction on the basis of the findings. Our case-based analysis provides empirical evidence that we can use different data mining classification algorithms to classify the dataset of agricultural regions on the basis of soil properties. Additionally, we have investigated the most performing algorithm having powerful prediction accuracy to recommend the best crop for better yield.