Abstract: Rapid, precise and non-destructive determination on moisture content of plant leaves have contributed to diagnosis of water deficiency. Ten plants were used as samples to detect their leaf capacitance and resistance by using the self-designed parallel-plate capacitor and improving resistance measuring method. Measured data was analyzed by SPSS 19.0 software for intraclass correlation coefficient (ICC) to verify reliability of the data. The leaves were divided into training set and test set. The training set was analyzed with Excel regression. The fitting equation was established among leaf moisture content, capacitance and resistance. The fitting equation was used to predict the leaf moisture content in the test set. It was shown that the data reliability of leaf capacitance among 10 plants was good. The data reliability of leaf resistance was good in Photinia × fraseri Dress and Myrica rubra (Lour.) S. et Zucc., and general in Ligustrum lucidum Ait., Sapindus mukorossi Gaertn, Cercis chinensis and Osmanthus sp., and poor in Viburnum odoratissinum. By Excel regression analysis, coefficient of determination (R²) was 0.978 8, adjusted R² was 0.977 4, significant value P=7.85×10^-37, fitting equation Z=86.0897-628.471X^-1-11.1753Y+117.2954Y·X^-1, the fitting effect of the model was good. The errors were -2.53%-1.46% compared with the drying method to predict the moisture content of test set with this model. This model could be used as a generic model to predict leaf moisture content of these 10 plants.

Key words: non-destructive inspection, capacitance, resistance, leaf moisture content, model