Supervised Machine Learning Techniques for Predicting Sugarcane Yield
Ramesh Medar1, Vijay S. Rajpurohit2
1Ramesh Medar, Department of Computer Science and Engineering, KLS Gogte Institute of Technology, Belagavi, India.
2Dr. Vijay S. Rajpurohit, Department of Computer Science and Engineering, KLS Gogte Institute of Technology, Belagavi, India.
Manuscript received on 01 March 2019 | Revised Manuscript received on 07 March 2019 | Manuscript published on 30 July 2019 | PP: 5662-5668 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2612078219/19©BEIESP | DOI: 10.35940/ijrte.B2612.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Agriculture is the most important sector in the Indian economy and contributes 18% of Gross Domestic Product (GDP). India is the second largest producer of sugarcane crop and produces about 20% of the world’s sugarcane. Sugarcane is cultivated in tropics and subtropic regions, on a wide range of soils from fertile well-drained mollisols to through heavy cracking vertisols, infertile acid oxisols, peaty histosols, to rocky andisols. Minimum moisture of 60cms, rich water supply and plenty of sunshine. In this paper, a novel approach to sugarcane yield forecasting in Karnataka, India region using Long Term Time Series (LTTS), weather-and-soil attributes, Normalized Vegetation Index (NDVI) and Supervised Machine Learning (SML) algorithms have been proposed. Sugarcane cultivation life cycle (SCLC) in the Karnataka region is about 12 months, with plantation beginning at three different seasons in weather condition. Our approach has been verified using historical dataset and results have shown that our approach has successfully modeled crop prediction. The application of the Custom-Kernel gives us a considerable boost in accuracy with SVM-Kernel Multiple giving 86.31% of accuracy, SVM-RBF kernel in second with an accuracy of 83.40%, GPR producing an accuracy score of 81.75%, Lasso giving an accuracy score of 26.81% and Kernel Ridge-RBF with the least accuracy score of 21.46% for final yield prediction.
Keywords: Agriculture, Machine learning, Custom-kernel, Crop prediction.
Scope of the Article: Machine learning