Fuzzy –Random Forest Predictive Models for Improvement of Sugarcane Yield and Quality

India is an important agricultural country and one in which sugarcane is so important as an agricultural crop because it is used to produce sugar, ethanol and bioenergy. Cultivation of this species is a major economic contributor, most particularly in Tamil Nadu, which is very important to national output. This study examines the relationship of meteorological parameters and soil conditions on the occurrence of pests and crop performance in order to increase sugar cane yield. Using predictive modeling and data analytics, the study examines historical meteorological and agricultural data from four different regions: Erode, Tiruvallur, Villupuram and Chittoor. Significant regional differences are shown with median production and quality scores, with Erode at 80 tons per hectare and 85 quality, and Tiruvallur at the lowest 50 tons per hectare. It underscores that region specific techniques specially customized for specific environmental difficulties (e.g. pest management and irrigation) are required. Consequences include targeted recommendations for improving crop outcomes and susceptibility to variability in climate, to facilitate the development of precision farming projects for sustainable sugarcane production.