Analysing Soil Data using Data Mining Classification Techniques

Background/Objectives: Soil is an essential key factor of agriculture. The objective of the work is to predict soil type using data mining classification techniques. Methods/Analysis: Soil type is predicted using data mining classification techniques such as JRip, J48 and Naive Bayes. These classifier algorithms are applied to extract the knowledge from soil data and two types of soil are considered such as Red and Black. Findings: In this paper, Data Mining and agricultural Data Mining are summarized. The JRip model can produce more reliable results of this data and the Kappa Statistics in the forecast were increased. Application/Improvement: For solving the issues in Big Data, efficient methods can be created that utilize Data Mining to enhance the exactness of classification of huge soil data sets.