Based on measured water contents and spectral reflectance of the bacterial spots on tomato leaves，we attempted to estimate the water contents of diseased leaves using the Gram-Schmidt transformation algorithm． The results show R₆₉₄ in visible and R₇₆₁，R₁₄₄₆，R₁₉₄₀，and R₂₄₉₀in near-infrared wavelengths were the spectra sensitive to the variations of water contents． Non-linear regression models were then developed to predict water contents using reflectance at R₁₉₄₀ and R₂₄₉₀ using Gram-Schmidt orthogonal transformation algorithm，with high R²(0． 724)，low relative error (0． 52%) and RMSE (0． 13)． This model was proved superior to the traditional linear model． The findings of this research can provide technical supports for diseases diagnosis of tomato plants under stress．