Tech Meets Tradition: Baramati’s Sugarcane Farming Through the Lens of AI

Reduced soil productivity, disease susceptibility, and water constraint are some of the major issues facing sugarcane farming in Baramati, Maharashtra.The integration of AI-powered satellite-driven pest and disease surveillance with the possibly disruptive future of subsurface drip irrigation (SDI) in conjunction with fertigation is thoroughly examined in this study.This study shows that integrated satellite monitoring combined with precision drip fertigation provides effective solutions for sustainable sugarcane production through analysis of field trials involving 1,000 farmers over 1,000 acres, satellite remote sensing data, deep learningbased disease detection achieving 98.2% accuracy, and economically validated case studies.The results show a 40% increase in yield, a 45% reduction in water use, 59% decrease in pesticides, and an improvement in profitability of 22.7%.The study offers technology frameworks that include IoT sensors, CNN and Vision Transformer illness detection models, NDVI satellite analysis, and economic viability.analysis.In order to maximize resource efficiency and climate adaptability, the future scope part examines how automated SDI with fertigation might deliver specific measures directly to root zones in response to stress identified by satellites.This integrated method shows a repeatable precision agriculture paradigm that combines cutting-edge AI technologies with conventional agricultural knowledge for fair, sustainable sugarcane growing.