DATA MINING TECHNIQUE TO ANALYZE SOIL NUTRIENTS BASED ON HYBRID CLASSIFICATION

Abstract

Data mining methods are greatly admired in the research field of agriculture. The agriculture factors weather, rain, soil, pesticides and fertilizers are the main responsible aspect to raise the production of yields. The fundamental basic key aspect of agriculture is Soil for crop growing. Examination of soil is a noteworthy part of soil asset management in horticulture. The soil investigation is exceptionally useful for cultivators to discover which sort of harvests to be developed in a specific soil condition. The main target of this work is to investigate soil supplements utilizing data mining classification techniques. A large data set of soil nutrients status database was collected from the Department of Agriculture, Cooperation and Farmers Welfare. The database contains measurement of soil nutrients for all different states. This work takes some district of Tamil Nadu in India to analyze the soil nutrients. Distinctive sort's soil has diverse variety of supplements. This paper chooses Nitrogen, Phosphorus, Potassium, Calcium, Magnesium, Sulfur, Iron, Zinc, and so forth, nutrients for investigating the soil supplements utilizing Naïve Bayes, Decision Tree and Hybrid approach of Naïve Bayes and Decision Tree. The performance of the classification algorithms are compared based on the following two factors: accuracy and execution time.