Optimizing_Food_Crop_Distribution_and_Nutritional_Support_through_Predictive_Analytics

Agriculture remains a cornerstone of food security and economic development; however, nutrient deficiencies in crops continue to challenge sustainable agricultural practices, particularly in Karnataka, India. This study leverages predictive analytics and machine learning—specifically Python-based linear regression—to identify nutrient shortfalls at the district level. By integrating crop production data, nutrient profiles, and regional dietary requirements, a surplus-deficit exchange framework was developed to optimize nutrient distribution. Initial results reveal significant nutrient gaps, which can be mitigated by up to 15% through targeted crop recommendations and district-level nutrient exchanges. The proposed exchange mechanism demonstrates over 85% operational efficiency. This research also paves the way for future enhancements, including real-time nutrient monitoring and AI-driven optimization models, supported by five-year nutrient forecast simulations to inform long-term agricultural policy and planning.