Optimizing Strategic Crop Yield Using Internet of Things (IoT) Technologies and Machine Learning: An Evaluative Study of Resource Efficiency

This research addresses the urgent need for precision agriculture by integrating Internet of Things (IoT) sensor networks with Machine Learning (ML) algorithms to overcome the resource waste and yield gaps inherent in traditional farming. The primary problem is the lack of unified frameworks that link real-time soil and microclimatic data streams with advanced predictive models like Random Forest and XGBoost. By deploying an experimental IoT testbed over a full agricultural season, the study evaluates statistical improvements in Water Use Efficiency (WUE), Nitrogen Use Efficiency (NUE), and overall crop yield compared to traditionally managed control plots. Prior literature demonstrates that such smart systems can reduce water consumption by up to 32% and achieve high predictive accuracy ($R^2 = 0.94$ vs $R^2 = 0.68$), filling a critical research gap by combining real-time field data with adaptive AI rather than analyzing these technologies in isolation.