Development of a microwave sensor for the non-invasive detection of plant responses to water stress: A practical application on maize (Zea mays L.)

In this study, a novel microwave sensing system, consisting of a microstrip self-resonant spiral coil inductively coupled to an external concentric planar probe loop, is presented and applied to the non-destructive detection of morpho-physiological plant responses to water stress. The optimised set-up of the proposed sensor ensures a highly sensitive spiral coil, which is a fundamental requirement to derive accurate information on plants' behavioural alterations related to water stress conditions. The proposed microwave sensor was tested it on two potted maize cultivars (Zea mays L.), namely “Cinquantino Bianchi” (CB) and “Scagliolo Frassine” (SF). For each cultivar, half of the samples were maintained at 100% (T100) field capacity while the other half was at 25% (T25) from 46 to 74 Days After Sowing (DAS). The frequency ($f_r$) shift and the amplitude peaks variation of the real component of the external planar probe input impedance (ℜ($Z_{input}$)) were obtained daily by positioning the sensor on the stem. These measured data were related to morpho-physiological parameters destructively acquired at four different growth stages. The resulting linear correlation between the stem's freshwater content (${FWC}_{stem}$) with both $f_r$ (r > −0.64) and the amplitude peaks (ℜ ($Z_{input}$)) (r > -0.70) provided evidence of the sensor's ability to identify stem dielectric properties' variations between the two water treatments. Concurrently, the sensor response demonstrated the capability to identify changes in the morphology and histology of the stem. Based on preliminary findings, the proposed sensor shows potential for employment in the real-time monitoring of plant water status, contributing to more economically and environmentally sustainable crop management practices. While the current correlations between plant water content and sensor measurements require further refinement to meet the rigorous industrial standards, nevertheless a large-scale adoption can be envisioned by leveraging IoT methodologies.