Soil Data Analysis Using Classification Techniques and Soil Attribute Prediction
This work addresses the need for better analysis of agricultural soil data, but it appears incremental as it applies existing data mining methods to a relatively new domain without claiming major breakthroughs.
The research applied data mining techniques to analyze soil datasets, focusing on classifying soil types using various algorithms and predicting untested attributes through regression, with implementation of automated soil sample classification.
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.