Estimation of the Influence of the N, P and K Fertilizer Nutrients Consumption on the Tur Yield in India using Data Mining Techniques
A.Vijay Kumar

Dr. A. Vijay Kumar* , Associate Professor, School of Computer Science and Engineering, Jain (Deemed to be University), Bangalore, Karnataka, India.
Manuscript received on March 13, 2019. | Revised Manuscript received on January 21, 2021. | Manuscript published on February 28, 2021. | PP: 47-51 | Volume-10 Issue-3, February 2021. | Retrieval Number: 100.1/ijeat.D6046048419 | DOI: 10.35940/ijeat.D6046.0210321
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Abstract: India has a great pressure to meet its food and other domestic need. The enormous increase in population despite of the decelerate in the increase rate of the population and significant income growth, insist an additional 2.5 million tons of food grains per annum according to the Agriculture policy: Vision 2020 document prepared by the Indian Agriculture Research Institute, New Delhi. The most needed food grains production in Indian is much less than that in the other areas of the world. Considering that the frontiers of expansion of cultivated area are almost closed in the region, the future increase in food production to meet the continuing high demand must come from the increase in yield. The factors that generally promote yield in India are fertilizer nutrients consumed, rainfall, temperature, pesticides sprayed, moisture in the air, moisture in the soil and humidity etc. Here an attempt is made to know the influence of the fertilizer nutrients consumed on the tur crop yield in India using data mining techniques so for which a dataset is prepared with the tur yield, fertilizer nutrients consumed like, Nitrogen, phosphorus, and potassium which are collected from the secondary sources like Department of agriculture and Cooperation, India and Fertilizer statistics, Fertilizer Association of India. The experiment results proved that there is an extremely negligible negative influence on the fertilizer nutrients consumed in India, during the study period. 
Keywords: Classification, Clustering, Correlation, Data Mining, Estimation, Regression.
Scope of the Article: Data Mining