Design and Implementation of Crop Yield Prediction and Fertilizer Utilization Using IoT and Machine Learning in Smart Agriculture Systems

The modernization of agricultural practices through smart agriculture has become crucial in meeting the growing food demand while conserving resources. Traditional farming methods often fall short in precisely managing fertilizer use and accurately predicting crop yields, leading to environmental harm and reduced agricultural efficiency. This paper introduces the Smart Agriculture Yield and Fertilizer Optimization System (SAYFOS), a novel approach designed to tackle these challenges. SAYFOS integrates advanced data analytics, Internet of Things (IoT) technologies, and machine learning algorithms to provide real-time monitoring and management of crop health and soil conditions. By analyzing data from soil sensors, weather forecasts, and satellite imagery, SAYFOS optimizes fertilizer application rates and predicts crop yields with high accuracy. Compared to conventional practices, SAYFOS improves crop productivity by 0.25%, reduces water usage by 0.33%, and enhances fertilizer efficiency by 0.25%. This system not only reduces the overuse of fertilizers, thereby minimizing environmental pollution but also enhances farmers’ decision-making processes, leading to increased crop yields and profitability. The implementation of SAYFOS represents a step forward in the transition towards more intelligent, sustainable, and productive agricultural practices.