Predicting Sugarcane Yields using Supervised Learning: A Comparative Study

Abstract

Bangladesh's sole source of white sugar is sugarcane, an agricultural commodity used to produce biofuels and other products. In recent years, ML techniques have been used to forecast yield, with encouraging outcomes. In this academic study, a supervised ML technique is suggested to forecast sugarcane yield from the perspective of Bangladesh. Weather patterns, sugarcane yield, and other relevant information are gathered from a variety of sources, including the BBS and the BMD. Many ML models, including Linear Regression, GBR, DTR, RFR, and XGBR, are trained. Different evaluation measures are used to compare the effectiveness of various models, such as MAE, MSE, and RMSE. The GBR model fared better than other models, according to the results. The results of this study can be used by farmers, policymakers, and the sugar industry to improve sugarcane yield in Bangladesh and make educated decisions. Future studies can investigate how to estimate sugarcane productivity in other areas and for other crops using ML approaches.