Irrigation Scheduling

Irrigation scheduling aims to optimize water use efficiency in agriculture and urban environments by precisely determining when and how much water to apply. Current research heavily utilizes machine learning, particularly deep learning models (e.g., neural networks, reinforcement learning) and advanced sensor networks, often incorporating data from remote sensing, weather forecasts, and even low-cost cameras, to predict soil moisture and estimate evapotranspiration. These advancements are improving water conservation, increasing crop yields, and reducing costs in various settings, from large-scale agricultural fields to individual residential lawns. The integration of these technologies is leading to more precise and data-driven irrigation strategies, ultimately contributing to sustainable water management.