Prediction and monitoring of leaf water content in soybean plants using terahertz time-domain spectroscopy

Leaf water content (LWC) is one of the physiological parameters most commonly used for describing crop growth status and productivity. Thus, rapid and non-destructive methods for the prediction of LWC are important. Here, a rapid and accurate LWC monitoring method using terahertz time-domain spectroscopy (THz-TDS) was tested on soybean. A high-precision mathematical model was developed to predict LWC based on the results of THz-TDS. Next, the model was used to study the effect of various levels of water -stress, different growth media, and leaf treatment with exogenous ABA. Reliable results showed that the correlation coefficient and root mean square error of the prediction set were 0.9153 and 0.0526, respectively. LWC gradually decreased over time under different moisture treatments, and even more slowly under conditions of normal water supply than under water stress. The trend followed by LWC under water deficit was dependent on the water-holding capacity of the growth medium. Soil had the best water-holding capacity, followed by the seedling matrix and the vermiculite matrix. Furthermore, the stomata of the adaxial and abaxial leaf surfaces closed due to water deficit, as determined by laser scanning confocal microscopy (LSCM). We found that stomatal opening in leaves significantly decreased under water stress to prevent excessive water loss, consistent with the observed reduction in leaf moisture content. Results indicated a rapid increase in LWC upon ABA treatment followed by a slower decrease. Similarly, changes in stomatal opening were observed using LSCM, consistent with the changes in leaf water content detected macroscopically. This study showed that THz radiation technology combined with modeling methods provides an effective, contact-free, safe, and non-destructive technique to measure water content in soybean leaves. This technique could facilitate further study of plant-water relations.