ELECTRICAL PROPERTIES PREDICT WHEAT LEAF MOISTURE

In this study, we aimed to establish a non-destructive and rapid approach to monitor the moisture content of wheat leaves in Southern Xinjiang, China, and promptly acquire information on the physiological water demand of crops to guide precision irrigation. Wheat leaves were used as the research object. Using a custom-made clamped parallel-plate capacitor and LCR digital bridge meter, we determined the electrical properties (capacitance and resistance) of wheat leaves with various moisture contents within a frequency range from 0.12 to 100 kHz. Moreover, we explored the correlation between leaf moisture content and the electrical properties. Our data showed that leaf moisture exhibited the best correlation with the electrical properties at 50 kHz. Under these optimized conditions, a model for moisture measurement was established using a single-parameter method (capacitance or resistance). However, the estimated standard errors (RMSE) of this method were 3.29% (for resistance) and 3.49% (for capacitance), which were greater than the standard error of the measured moisture content (2%). Therefore, we developed an improved model using a two-parameter method (capacitance and resistance), and the estimated standard error was 2.68%, which was more feasible for predicting moisture content compared with the single- parameter method. The model was validated using eight groups of wheat leaf samples at the turning-green stage and the jointing stage, and the RMSE values were less than 2%. Our findings provide a scientific basis for real-time and targeted water-saving irrigation of wheat in arid areas of Southern Xinjiang.

Automated summary

This study aimed to establish a non-destructive and rapid approach to monitor the moisture content of wheat leaves in Southern Xinjiang, China. The research utilized a custom-made clamped parallel-plate capacitor and LCR digital bridge meter to determine the electrical properties of wheat leaves and establish a model for predicting moisture content. The improved two-parameter method showed lower estimated standard errors compared to the single-parameter method, providing a scientific basis for real-time and targeted water-saving irrigation in arid areas.