[Objective]The study aimed to realize the noninvasive detection of water content of cabbage leaves and provide reference for field water management of cabbage.[Method]In the study,a total of 6 treatments including 23.33 L/plant(CK),11.33 L/plant(W1),15.33 L/plant(W2),19.33 L/plant(W3),27.33 L/plant(W4)and 28.33 L/plant(W5)were set up.The spectral data of 99 cabbage leaf samples at rosette stage and heading stage were respectively collected.Savitzky-Golay,moving average,normalization and multiple scattering correction were used for sample pretreatment,successive projection algorithm were used for feature extraction,and multiple linear regression was used for modeling.[Result]In the range of 400-1000 nanometers,the prediction accuracy of water content model of cabbage leaves with different amounts of irrigation had significant difference.The accuracy of the prediction models established by the original spectra after pre-treatment had a certain degree of improvement,among which Savitzky-Golay had the best pretreatment effect.The feature extraction method improved the quality and accuracy of spectral date.In rosette stage and heading stage,the continuous projection algorithm was selected to ex-tract the feature bands as the best,5 and 6 characteristic wavelengths were selected respectively,and the characteristic wavelengths of the two periods were basically in a certain water sensitive band range.In rosette stage and heading stage,the prediction model of water content of cab-bage leaves established by multiple linear regression had higher prediction accuracy than principal component regression and partial least squares regression.The correlation coefficient of calibration set and root-mean-square error of multiple liner regression model were 0.8927 and 0.8757 in rosette stage,and 0.9167 and 0.9014 in heading stage.[Conclusion]Hyperspectral technology can successfully monitor the water content of cabbage leaves in different growth stages and provide reference for farmland water management and technical support for precision irrigation.
HyperspectralCabbageLeaf water contentModeling analysisPrecision irrigation
NETL
NSTL
万方数据
