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IJSTR >> Volume 9 - Issue 6, June 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Soil Data Classification Using Attribute Group Rank with Filter Based Instance Selection Model

[Full Text]

 

AUTHOR(S)

G.Murugesan, Dr. B.Radha

 

KEYWORDS

Agriculture productivity, soil data, attribute group rank, instance selection-based learning, macronutrients, micronutrients, classification accuracy, filter based instance selection.

 

ABSTRACT

Due to the advancement of automation through data mining and machine learning algorithms, research on agricultural components such as soil, crops, rainfall and price prediction have gained massive attraction from research communities. Data mining along with machine learning techniques have become the most dominating field employed in almost all the research areas pertaining to knowledge acquisition. The nutrient status of the soil along with environmental and climatic conditions are directly involved in agricultural production. Though the farmers have wide practical knowledge about the crops, the natural changes happening at the earth's surface and unpredictable climatic changes and rainfall normally do not support crop productivity. In agriculture, the soil is the foremost important factor that includes several physical parameters such as pH value, organic carbon present in the soil along with primary macronutrients and secondary micronutrients and thus the knowledge about the quality of soil reveals the type of crops to be cultivated and the amount of yield produced. In this paper, a novel classification algorithm is proposed that uses attribute group rank with filter-based instance selection for effectively classifying the soil data. Experiments have been made with the soil data of the Pollachi region in Coimbatore district, Tamil Nadu state, India which is a popular market place for various grains, vegetables, and fruits. The classification accuracy of the proposed model is also compared with the other classification models. From the result analysis, it is proved that the proposed model provides a better accuracy rate for soil data.

 

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