Soil Fertility Prediction for Yield Productivity and Identifying the Hidden Factors through Machine Learning Algorithms
DOI:
https://doi.org/10.26438/ijcse/v7i1.596600Keywords:
Agriculture, Classification, Data Mining, J48, Naïve Bayes, REPTree, Soil fertilityAbstract
Data mining is a promising technology which helps to analyze the data and to discover the interesting hidden patterns in large volume of data. The goal of data mining is to predict, identify, classify and optimize the use of resources to recognize complex patterns and make intelligent decisions based on data. Agriculture plays a vital role in economy and it is the backbone of our economic system. Data mining in agriculture provides many opportunities for exploring hidden patterns in these collections of data. Soil Fertility is the capability of soil to provide plants with enough nutrients and moisture to yield crop in better way. The yielding capability of a soil depends on soil fertility. It is very important to achieve and maintain an appropriate level of soil fertility for crop production. The main focus of this paper is to analyse the soil data which is collected from soil testing laboratory and identifying attributes to predict fertility from collected dataset by using different Machine Learning algorithms. This work also focuses on finding the best classification algorithm based on accuracy and performance measure using the soil dataset with different Data Mining classifiers like J48, Naïve Bayes and REPTree.
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