Digital Twin-Based Smart Irrigation for Sustainable Water Management in Arid Areas

Water scarcity is a critical issue in desert regions due to limited precipitation. To address this, it is essential to minimize water losses and implement efficient irrigation strategies. Digital Twins (DT), empowered by the Internet of Things (IoT), have shown effectiveness in virtual environments by enabling agronomists to remotely manage irrigation using real-time data, reducing reliance on manual observation. This study integrates IoT, DT, and Deep Learning (DL) to develop a resilient and reliable system for data analytics and irrigation scheduling. The system predicts optimal irrigation times based on environmental variables such as temperature, soil moisture, and humidity. These factors are used to estimate evaporation, which directly influences the amount and timing of water application. Experimental results under various weather conditions demonstrated the system’s competitive performance.