Assessing olive tree (Olea europaea L.) responses to water shortage through radio frequency sensors

This study presents the application of advanced radio frequency (RF) sensors for non-invasive, plant structure-specific water stress monitoring in olive trees (Olea europaea L.), focusing on the cultivars Frantoio and Leccino, known for their differing water-use strategies. The sensing system comprises circular and double-layer rectangular spiral RF sensors, optimised to maximise the quality factor (Q-factor) for enhanced sensitivity. The double-layer design, where one layer is “left-handed” and the other “right-handed,” allows for an increased magnetic field and detection reliability, especially on small branches where signal stability can be challenging. Throughout an 88-day experimental period, olive trees were subjected to full irrigation (FI) and deficit irrigation (DI) treatments. RF sensors were placed on the olive plants trunks and branches to capture plant structure-specific stress responses, with measurements recorded weekly. In the Frantoio cultivar, resonance frequency shifts were pronounced under DI, especially in the trunk and large branches, where notable physiological changes were observed. Correlations were established between resonance frequency data and morpho-physiological indicators such as trunk diameter increment (SDI) and fresh water content (FWC), validating the sensor's sensitivity to dielectric property variations due to water stress. Anatomical analyses further revealed tissue adaptations in Frantoio under DI, including increased bark and cortex thickness and intensified sclerenchyma fibre formation, indicative of structural changes to support water transport. In contrast, the Leccino cultivar showed minimal frequency variations and lacked significant anatomical alterations, reflecting its conservative water-use strategy and limited sensitivity to stress. This research confirms RF sensors’ potential as precise tools for early water stress detection in olive trees, with an emphasis on sensor placement on main plant structures and sensitivity optimization to enhance accuracy. These findings support the use of RF sensing systems in precision agriculture for sustainable irrigation management, especially in water-limited environments and conditions.