Electrical Properties Predict Wheat Leaf Moisture

Highlights A non-destructive prediction model for moisture content of wheat leaves was established based on electrical properties. The model based on a single property (capacitance or resistance) was improved by using both properties. The model accurately detected the moisture content of wheat leave...

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Veröffentlicht in:Transactions of the ASABE 2021, Vol.64 (3), p.929-936
Hauptverfasser: Hao, Yumei, Hua, Yuantao, Li, Xu, Gao, Xianqiang, Chen, Jilong
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Sprache:eng
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Zusammenfassung:Highlights A non-destructive prediction model for moisture content of wheat leaves was established based on electrical properties. The model based on a single property (capacitance or resistance) was improved by using both properties. The model accurately detected the moisture content of wheat leaves in real-time to avoid irrigation lag. The results provide a basis for real-time and targeted water-saving irrigation of winter wheat in an arid region. Abstract . 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. Keywords: Electrical property, Model, Moisture content, Precision irrigation, Wheat leaves.
ISSN:2151-0040
2769-3295
2151-0040
2769-3287
DOI:10.13031/trans.14210