Abstract: In order to predict soybean pods anthracnose early and effectively, Visible/near infrared (Vis/NIR) spectra technology combined with successive projections algorithm (SPA) and least square support vector machines (LS-SVM) was investigated for the rapid and non-destructive discrimination of such soybean disease. Total 194 samples were collected, the best partial least squares (PLS) model was established comparing with the different pretreatment methods. The principal component analysis (PCA) was used to extract the best principal components (PCs), and the SPA was used to extract the effective wavelengths. The best PCs and the effective wavelengths were respectively used as input variables for the PCA-LS-SVM and SPA-LS-SVM disease detection models. The validation set indicated that both models had acceptable accuracy rate, especially SPA-LS-SVM model has an accuracy rate of 95.45% in predicting fungal infections. According to the results, SPA was a powerful way for the effective wavelengths selection, and Vis/NIR spectroscopy was feasible for the identification of colletotrichum truncatum on soybean pods. There is a potential to establish an online field application of early plant disease detection based on visible and near-infrared spectroscopy.