Abstract: Multi-spectral image analysis method was utilized to quantitatively analyze the rape moisture content for the nondestructive testing of rape water stress. Median-filtering method was used to preprocess the images. Two dimensional maximum entropy segment approach was used to complete background segmentation of multi-spectral images. The mean & ratio features of multi-spectral images of rape canopy were extracted. It was found that the features of the image mean value at 560, 960, 810 nm and the 960 nm/810 nm ratio were highly correlated with the rape moisture content during the rape’s whole growth period. With the consideration of the existence of the multi-collinearity among the multi-spectral variables, the prediction model of moisture content of rape in different growth phases was built by stepwise regression method. The result showed that the multi-spectral image prediction method can be used to quantitatively analyze the rape moisture content. The correlation coefficient between the predicted value and the measured one was 0.83, and the RMSE was 4.52%. The average relative error was less than 8% in the seeding stage. The prediction model in this study may provide scientific evidence for water-efficient irrigation.