Implementation of a microwave sensor for the non-destructive detection of plant water stress

V Lazzoni, R Rossi, D Brizi, F Ugolini… - 2023 17th European …, 2023 - ieeexplore.ieee.org
2023 17th European Conference on Antennas and Propagation (EuCAP), 2023ieeexplore.ieee.org
In this paper, the preliminary results from a measurement campaign of a novel sensor for
plants hydric stress monitoring are presented. The proposed microwave sensing system,
working at a frequency of about 250 MHz, consists in a microstrip self-resonant spiral coil
inductively coupled to an external concentric planar probe loop. The microwave sensing
configuration is the result of an optimization procedure aimed at maximizing the spiral coil Q-
factor, required to obtain high sensitivity. The experiments have been conducted on 22 …
In this paper, the preliminary results from a measurement campaign of a novel sensor for plants hydric stress monitoring are presented. The proposed microwave sensing system, working at a frequency of about 250 MHz, consists in a microstrip self-resonant spiral coil inductively coupled to an external concentric planar probe loop. The microwave sensing configuration is the result of an optimization procedure aimed at maximizing the spiral coil Q-factor, required to obtain high sensitivity. The experiments have been conducted on 22 maize plants (Zea mays L.) randomly divided into two water treatments: T25 (applying 25% of the irrigation requirements) and T100 (full watering). In particular, plant responses to soil water depletion were detected by monitoring the amplitude and frequency shift variation of the external planar probe input impedance acquired by the sensor. In accordance with the theoretical expectations, we detected an upshift in the inner spiral resonant frequency and a rise in the probe loop input impedance amplitude as irrigation water decreased. The obtained experimental results encourage future research, especially envisioning applications in precision agriculture. Indeed, the sensor might be employed to detect and real-time monitor the health status of maize plants, optimizing the resource deployment strategy accordingly.
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