Applying Naive Bayes Classification Technique for Classification of Improved Agricultural Land soils

Abstract

India is predominantly an farming country. Indian soil is classified into two diverse types North Indian Soil and South Indian soil. We have separated it into two basically dissimilar sets are north Indian soil and South Indian Soil. North Indian Soil is the Northern India plains are habitually created of deep alluvial soil. The top soil varies in surface from sand to clay, the larger part being light loam, leaky in texture, simply worked and in nature fruitful. South Indian soil is the Southern India peninsular earth plane is prepared up of hills and river valleys. Hilly tracts are naturally not fitting for farming, some moorland are very warm [Prakash,2015]. The objective of this work is to classify soil types by their characteristics and fertility, predict soil types through classification technique like naïve bayes and also compared this classification technique to among classification technique like zeroR and stacking. This research goal is to judge new data mining techniques apply on soil database for classification of soil and in addition contrast the naïve bayes classifier to another classifier [3]. Soil database is hiered from multiple soil websites like Krishi vigyan kendra, Krishiworld.com, farmer.gov.in and krishijagran.com.