AI-Driven IoT-Enabled Soil Nutrient and Moisture Monitoring with XGBoost and LightGBM-Based Predictive Irrigation and Fertilization Optimization for Sustainable Precision Agriculture

In agriculture, soil moisture is essential because it affects crop productivity, nutrient absorption, and plant health. Both productivity and sustainability are negatively impacted by the inefficiency of water utilization that results from traditional irrigation practices. Optimizing irrigation and fertilization may be achieved using a data-driven strategy made possible by artificial intelligence (AI) and the Internet of Things (IoT). Using an ESP32 microcontroller and Internet of Things (IoT) sensors for soil temperature, moisture, and NPK, this article demonstrates an AI-powered soil monitoring system. Data is sent to cloud platforms so it may be processed in real-time. Soil conditions are analyzed by machine learning models like XGBoost and LightGBM, which then forecast the best times to water and provide specific fertilizing recommendations. The technology constantly adjusts to shifting soil conditions by using gradient boosting techniques, which increases farming productivity. Solar power ensures operation even in distant places, and the system has a web-based and mobile interface for real-time monitoring. Improved resource optimization, higher crop yields, and sustainable farming methods are the results of precision agriculture, which is made possible by the integration of AI-driven predictive modeling with IoT-based monitoring.