Soil Data Analysis Using Classification Techniques and Soil Attribute Prediction

TL;DR

This paper explores the application of data mining techniques for soil data analysis, focusing on classifying soil types and predicting untested soil attributes to enhance agricultural research and automation.

Contribution

It introduces the use of classification and regression techniques for soil data analysis, a relatively new approach in agricultural soil research.

Findings

Effective soil classification achieved using various algorithms.

Successful prediction of untested soil attributes.

Automated soil sample classification implemented.

Abstract

Agricultural research has been profited by technical advances such as automation, data mining. Today, data mining is used in a vast areas and many off-the-shelf data mining system products and domain specific data mining application soft wares are available, but data mining in agricultural soil datasets is a relatively a young research field. The large amounts of data that are nowadays virtually harvested along with the crops have to be analyzed and should be used to their full extent. This research aims at analysis of soil dataset using data mining techniques. It focuses on classification of soil using various algorithms available. Another important purpose is to predict untested attributes using regression technique, and implementation of automated soil sample classification.