Growth Prediction using IoT-based Smart Irrigation Systems

This paper introduces "Smart Irrigation Yield Optimization (SIYO)," a sophisticated IoT-based smart irrigation system aimed at significantly improving crop yield and growth predictions. Traditional irrigation methods such as furrow irrigation, overhead sprinkler systems, and manual watering are analyzed for their inherent inefficiencies, especially in precision and resource management. SIYO overcomes these drawbacks by employing a network of realtime sensors that monitor soil moisture, temperature, humidity, and nutrient levels, thus enabling precise and automated irrigation tailored to the specific needs of crops. This technology not only optimizes water and nutrient usage but also adapts to varying environmental conditions to maximize crop potential. Comparative studies reveal that SIYO enhances water efficiency by 0.30% over furrow irrigation, increases nutrient utilization by 0.25% compared to overhead systems, and boosts the accuracy of crop yield and growth predictions by 0.40% against manual methods. The implementation of SIYO demonstrates the substantial improvements in agricultural productivity and resource sustainability achievable with advanced IoT technologies.