Novel Framework for Quality Crop Predictions Using Data Mining and Soft Computing Techniques

Data mining (DM) and Soft Computing (SC) are a vital computational approach that offers good competence of flexible agricultural data processing systems to solve farmer’s problems. Recently, soft computing has emerged as a powerful technique for solving and analyzing complex real-world problems. This article suggests an approach of smart crop predictions is presented through DM &SC in the field of agricultural quality crop prediction. A five-level framework is proposed namely 1) Collection of data from different repositories, 2) Pre-processing of data, 3) Appropriate Classifier Selection, 4) Prediction and Estimation 5) Draw AUC & ROC curve. Method proposed here focuses on analyzing agricultural yield, soil for crop, rainfall required based on chemical property of soil. Agricultural data analysis and cataloging is one of the best applications of new computing tools such as soft Computing, Machine Learning (ML) approach, became a burning area for the reason that of the massive development of farming data. DM& SC approaches for accomplishing applied research will give effective answers for this type of problem. ML is a working tool to study multiple learners and combine their assessments for accomplishing greater forecasting accurateness. In this investigation, we had recapitulated ML methods which can be applied as an essential tool by the farmer and agriculture scientist for timely prediction of crop production.