AI-DRIVEN SOIL MANAGEMENT SYSTEM FOR ORGANIC FARMING USING MACHINE LEARNING

Organic farming is gaining global attention as a sustainable alternative to conventional agriculture,emphasizing soil health, biodiversity, and environmental conservation. However, organic farmers often lackaccess to digital tools that can provide personalized, data-driven recommendations. This research introducesan AI-powered soil management system designed to bridge this gap. The system utilizes machine learningalgorithms to analyze key soil health indicators such as pH, nitrogen, phosphorus, potassium, organic carbon,and moisture content. Based on this analysis, it suggests the most suitable crops and organic fertilizers thatalign with the ecological principles of organic farming. Unlike existing systems that depend on chemicalinputs, IoT devices, or continuous internet connectivity, the proposed solution is lightweight, scalable,offline-compatible, and sensor-free, making it accessible even in remote or resource-constrained regions.The model is designed to improve over time through continuous learning from user feedback and regionaldata, ensuring context-aware and accurate recommendations. By integrating AI with sustainable agriculturalpractices, this research contributes a novel, cost-effective, and eco-conscious approach to enhancing soilproductivity and supporting the long-term success of organic farming.