Open AccessElectrical Properties Predict Wheat Leaf Moisture
Author(s) -
Yumei Hao,
Yuantao Hua,
Li Xu,
Xianqiang Gao,
JiLong Chen
Publication year - 2021
Publication title -
transactions of the asabe
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.396
H-Index - 101
eISSN - 2151-0040
pISSN - 2151-0032
DOI - 10.13031/trans.14210
Subject(s) - capacitance , water content , moisture , environmental science , irrigation , electrical resistance and conductance , lcr meter , soil science , materials science , agronomy , composite material , chemistry , engineering , geotechnical engineering , electrode , biology
HighlightsA 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.