Hyperspectral imaging feature of chlorophyll content (SPAD) in tomato leaves stressed by grey mold was studied in the present paper. Hyperspectral imagings of healthy and infected tomato leaves were obtained by hyperspectral imaging system from 380 to 1 030 nm and diffuse spectral response of region of interest (ROI) from hyperspectral imaging was extracted by ENVI software, then different preprocessing methods were used including smoothing and normalization etc. The partial least squares regress (PLSR) and principal component regress (PCR) models were developed for the prediction of SPAD value in tomato leaves based on normalization preprocessing method, then the back-propagation neural network (BPNN) and least squares-support vector machine(LS-SVM)models were built based on the four variables suggested by PLSR model. Among the four models, LS-SVM model was the best to predict SPAD value and the coefficient of determination (R2) was 0.901 8 with the root mean square error of prediction (RMSEP) of 2.599 2. It was demonstrated that chlorophyll content (SPAD) in healthy and infected tomato leaves can be effectively detected by the hyperspectral imaging technique.