Sugarcane crop yield forecasting model using supervised machine learning

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.