A theoretical framework for vineyard irrigation management using AI-driven sensor systems

This study presents a theoretical framework for applying artificial intelligence to vineyard irrigation management, aiming at optimizing water use and minimizing waste. The system design provides the field implementation of three types of sensors (soil temperature, soil moisture, and solar radiation) connected to a data logger, with data processed by AI. Collected data are used to estimate evapotranspiration via the Hargreaves-Samani formula and to determine the optimal irrigation timing. A proof of concept, based on ten scenarios, was carried out to assess the system’s responsiveness. Results highlighted that the AI consistently identifies anomalous values, estimates water needs, and suggests targeted interventions. The system has the potential to produce benefits as it improves efficiency and sustainability, by reducing water overuse and the related environmental and economic costs. Future developments include designing a physical prototype and integrating external weather station data into the system.