In Situ Nondestructive Monitoring of Maize Leaf Turgor Pressure Using LPCP Sensors: Drought Stress Stage Classification and Meteorological-Driven Simulation Model

In situ nondestructive continuous monitoring of plant water status coupled with online data analysis is a core technical prerequisite for developing intelligent irrigation decision-making systems. The LPCP (leaf patch clamp pressure probe, ZIM-probe) sensor can detect leaf turgor pressure and shows promising application prospects. In this paper, the LPCP was used to monitor the leaf turgor pressure of maize during the silky growth stage cultivated in the North China Plain. The experiment was arranged in a randomized block design with two treatments: full irrigation (CK) and natural drought (ND). The results showed that the probe output pressure (Pp) underwent three stages: Pp min increasing stage, Pp max early-occurring stage, and Pp curve inversion stage, along with soil water decreasing, which correspond to mild, moderate, and severe water stress, respectively, and the ranges of soil and leaf water content of each stage were identified. As water stress intensified, the peak times of transpiration rate and stomatal conductance occurred earlier than normal, and the values decreased; meanwhile, the relationships between Pp, sap flow (SF), and leaf physiology indicators were quadratic parabolic, but the parabola opening directions, determination coefficients of regression equations, and model significance differed under different water stress stages. Under mild and moderate water stress, Pp positively correlated with SF, vapor pressure deficit (VPD), photosynthetically active radiation (PAR), and air temperature (T), while negatively correlated with relative humidity (RH), and path analysis results revealed that PAR and T exerted direct effects on Pp variations, whereas SF and VPD influenced Pp indirectly through other variables, and RH exhibited a negative effect on Pp changes. However, these correlations reversed under severe water stress. A regression model including PAR, T and VPD was established to simulate the Pp values of maize under full irrigation conditions, and by comparing the variation trend of Pp curves between the predicted and the measured values, whether maize was under water stress could be determined.