Integrated IOT-Driven Decision Support System for Sustainable Soil and Crop Management in Indian Agriculture

Abstract - An integrated, research-based decision support system for sustainable crop and soil management in Indian agriculture is presented in this project. The platform provides actionable recommendations for fertilizer application, crop rotation, and yield prediction across rice, wheat, pulses, oilseeds, vegetables, and millets by fusing real-time IoT sensor data (measuring soil moisture, pH, NPK, and enzyme activities) with comprehensive historical datasets from ICAR, Soil Health Card, and satellite sources. In order to improve soil health, forecast yields, and optimize nutrient management, sophisticated machine learning models evaluate both historical and real-time data. Compatibility matrices are used by the system's crop rotation engine to recommend sequences that enhance soil C:N:P balance, reduce pests, and boost climate variability resilience.The solution, which is scalable and reasonably priced, helps smallholder and institutional users achieve greater productivity and long-term sustainability by bridging the gap between state-of-the-art agronomic research and real-world farm management. Keywords: crop rotation, fertilizer recommendation, soil management, sustainable agriculture, yield prediction, and decision support system.