Vijay S. Rajpurohit

Work place: KLS Gogte Institute of Technology, Belagavi, 590008, India



Research Interests: Image Processing, Computing Platform, Data Structures and Algorithms


Dr. Vijay S Rajpurohit, working as Professor in the Department of Computer Science and Engg at Gogte Institute of Technology, Belagavi, Karnataka, India. Completed B.E. in Computer Science and Engg. from Karnataka University Dharwad, M.Tech. at N.I.T.K Surathkal and Ph.D. from Manipal University, Manipal in 2009. His research areas include Image Processing, Cloud Computing, and Data Analytics. He has published a good number of papers in Journals, International and National conferences. Dr. V. S. Rajpurohit is the reviewer for a few international journals and conferences. He is the associate editor for two international journals and Senior Member of the International Association of CS and IT. He is also the life member of SSI, ISC and ISTE associations.

Author Articles
Sugarcane Crop Yield Forecasting Model Using Supervised Machine Learning

By Ramesh A. Medar Vijay S. Rajpurohit Anand M. Ambekar

DOI:, Pub. Date: 8 Aug. 2019

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. 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 Karnataka(India) region is about 12 months, with plantation beginning at three different seasons. Our approach divides yield forecasting into three stages, i)soil-and-weather attributes are predicted for the duration of SCLC, ii)NDVI is predicted using Support Vector Machine Regression (SVR) algorithm by considering soil-and-weather attributes as input, iii)sugarcane crop is predicted using SVR by considering NDVI as input. Our approach has been verified using historical dataset and results have shown that our approach has successfully modeled soil and weather attributes prediction as 24 steps LTTS with accuracy of 85.24% for Soil Temperature given by Lasso algorithm, 85.372% accuracy for Temperature given by Naive-Bayes algorithm, accuracy for Soil Moisture is 77.46% given by Naive-Bayes, NDVI prediction with accuracy of 89.97% given by SVR-RBF, crop prediction with accuracy of 83.49% given by SVR-RBF.

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