Abstract: Abstract: Water is essential for plant growth, and water shortage will have an impact on plant yield, growth and quality, therefore, rapid and nondestructive detection of water content in maize leaves is of great significance for scientific guidance of irrigation, and it is also important for improving crop yield. In order to rapidly detect the moisture content of maize leaves, transmission spectrum on the band of 300-1 700 nm was selected to predict moisture content of maize leaves. The testing system included the light source and sensors. The light source part was provided by Wuling Optical Instrument Company. The transmission spectral range of 350-820 nm was obtained with ocean STS-VIS spectrometer, and the spectrum of 900-1 700 nm was measured by Wuling NIRez near-infrared spectrometer. Although the 820-900 nm transmission spectrum was absent, the transmittance curves obtained from different water gradients showed the same trend. Ten maize plants at stage V9 were detected. In order to eliminate the interference of different blade thickness on the experimental results, the first piece of fully expanded leaves from the top of the plant was selected as the experimental sample. The samples were taken back to the laboratory, and 10 cm long strips were cut along the veins from the middle of the leaves. The experiment measured and recorded transmission spectra curve and the moisture content of maize leaves under different water gradient. The moisture content measured by fresh weight moisture content formula, the transmission rate calculated according to Lambert-Beer law. The total number of experimental samples was 98, validation set was 33, and modeling set was 65. The total moisture content of maize leaves was 10%-85% and the average moisture content was 57.9%. In order to eliminate the influence of the noise caused by the spectrometer itself, the Savitzky-Golay method was used for pretreatment. The data was processed using correlation analysis between the transmission spectra and the moisture content, and principal co. As a result, the sensitive wavelengths at 800, 932 and 1 423 nm were selected by the correlation analysis, and the sensitive wavelengths at 478, 748, 1058 and 1323 nm were extracted by principal component analysis. To further improve the determination ability of the model, four sensitive wavelengths were selected, which were 800, 1 323, 1 058 and 1 423 nm. Using the combination of these four wavelengths, 12 vegetation index such as ratio vegetation index, difference vegetation index and normalized difference vegetation index were obtained. In the 12 vegetation indices, the modeling accuracy and prediction accuracy were significantly higher than other vegetation indices, so the DVI (1 423, 800) and DVI (1 423, 1 503) were respectively used to predict the moisture content. The results showed that DVI, which was derived from the combination of strong water absorption band (1 423 nm) and weak water absorption wavelength (800 and 1503 nm), could effectively restrain the disturbance caused by structural changes, and it could sensitively reflect the moisture content of maize leaves. Multivariate linear regression model was established based on DVI (1 423, 800), T1323 and T1058, the calibration R2 reached to 0.968 8, the validation R2 reached to 0.951 9, and the root mean square error of prediction was 0.061. The results showed that water prediction model established by transmission spectroscopy could provide efficient guidance for plant leaf water rapid detecting instrument development.