Early detection of early blight on tomato leaves using hyperspectral imaging technique based on spectroscopy and texture was researched in the present study. Hyperspectral images of seventy-one infected and eighty-eight healthy tomato samples were captured by hyperspectral imaging system over the wavelength region of 380～1 030 nm and then were dimensioned by principal component analysis (PCA). Diffuse spectral response of region of interest (ROI) from hyperspectral image was extracted by ENVI software. At the same time, four features variables were extracted by texture analysis based on gray level co-occurrence matrix (GLCM) from each PC image of the first eight PCs including contrast, correlation, entropy and homogeneity, respectively. Then PCA and successive projections algorithm (SPA) were used to build least squares-support vector machine (LS-SVM) model to detect early blight on tomato leaves. Among the six models, LS-SVM model based on spectroscopy performed best with the discrimination of 100%. It was demonstrated that it is feasible to detect early blight on tomato leaves by hyperspectral imaging technique.