Implementation of Association Rule Mining for different soil types in Agriculture

Abstract: Agriculture sector is the mainstay and backbone of the Indian economy. Despite the focus on industrialisation, agriculture remains a main sector of the Indian economy both in terms of contribution to gross domestic product (GDP) as well as a source of employment to millions across the country. The total Share of Agriculture & Allied Sectors (Including livestock, agriculture, forestry and fishery sub sectors) in terms of percentage of GDP is 13.9 percent during 2013-2014 at 2004-2005 prices. Agricultural exports constitute a fifth of the total exports of the country. At 157.35 million hectares, India holds the second leading agricultural land worldwide. All the 15 major climates are initiated in India and the country also possess 45 of the 60 soil types in the earth. India is the biggest producer of milk, tea, pulses, cashew and jute, and the second biggest producer of rice, wheat, fruits and vegetables, cotton, sugarcane and oilseeds [1]. In agricultural decision production process, both weather and soil characteristics plays a vital role. This research aimed to assess a variety of association techniques of data mining and apply them to a soil science database to establish if meaningful relationships can be created. A huge data set of soil database is extracted from the Soil Science India. This paper provides the data mining association techniques used in agriculture which includes Apriori.

Keywords: association, apriori algorithm, agriculture, soil types.