DEVELOPMENT OF INNOVATIVE SENSORS FOR DETECTING THE WATER STATUS OF PLANTS IN PRECISION AGRICULTURE APPLICATIONS

This doctoral research addresses the development of innovative sensors and integrated systems for the direct assessment of plant water status, a key parameter in precision agriculture and sustainable irrigation. The thesis is organized into three main experimental parts, each addressing a different level of plant-based monitoring, from direct leaf sensing to whole-plant motion and irrigation automation. Unlike conventional approaches that infer water availability indirectly from soil or environmental measurements, this work focuses on plant-based sensing, enabling real-time and non-invasive detection of water stress. Four complementary approaches were investigated: (i) electrical impedance spectroscopy (EIS) of leaf tissues to characterize physiological and structural changes linked to hydration levels; (ii) optical sensing through near-infrared (NIR) spectroscopy, exploiting absorption variations related to leaf water content; (iii) monitoring of petiole movements using low-power inertial measurement units (IMUs) to capture circadian and stress-induced motion patterns; and (iv) the design and validation of a smart irrigation platform (SPRITZ project) integrating soil and environmental sensors, wireless communication, and automated control via cloudbased IoT infrastructure. Dedicated electronic boards, embedded firmware, and LoRa-based communication modules were developed to ensure reliable, energy-efficient data acquisition under both controlled greenhouse and outdoor field conditions. Experimental campaigns were carried out on multiple plant species, subjected to differentiated irrigation regimes, to evaluate sensor performance and their correlation with reference physiological indicators such as relative water content (RWC). Data processing combined statistical analysis, modeling, and machine learning algorithms to extract relationships between sensor outputs and plant water dynamics. Results demonstrated the feasibility and the robustness of the proposed sensing methods, showing that plant-centric measurements can effectively discriminate between hydration levels and environmental treatments. Overall, this research establishes a comprehensive framework for plant-based IoT sensing, bridging physiological monitoring with automated irrigation control. The proposed technologies provide a foundation for future multi-sensor fusion systems capable of predictive water management, thereby contributing to improved water-use efficiency and the sustainability of modern agriculture.