Efficient fertilizer management plays a critical role in maximizing crop yield while reducing environmental harm and minimizing resource waste. This study presents an IoT-based intelligent fertilizer recommendation system designed to deliver accurate, real-time application guidance. The system integrates NPK sensors for soil nutrient detection, environmental sensors for humidity and temperature monitoring, and rain gauges to collect precipitation data. Data from the field is transmitted through an Arduino microcontroller to a cloud platform. A Random Forest classifier is used to determine the need for fertilization, while a CatBoost regressor estimates the required fertilizer quantity. The system was tested using real-time field data across 22 crop types, achieving 100% accuracy in classification and strong performance in regression tasks. Recommendations are automated and delivered via SMS to streamline field operations. The objective of this study is to develop an automated, sensor-driven fertilizer recommendation system using machine learning for precision agriculture. The novelty lies in the integration of real-time IoT sensing with hybrid AI models to optimize fertilizer use. This approach enhances productivity, reduces input waste, and supports environmentally sustainable farming.
Chaudary et al. (Tue,) studied this question.