Abstract: In order to seek a quick and efficient detection method of edamame, hyperspectral imaging technique was applied to the nondestructive detection of insect-damaged edamame in this study. It was well known that the ROI of the vegetable soybean pods is the position of the beans, A ROI selection approach based on the mean gray values in the horizontal coordinate and vertical coordinate was proposed. In this experiment, hyperspectral transmission images were acquired from normal and insect-damaged vegetable soybeans (225beans) , These beans were used as the research samples. First, a region of interest (ROI) of edamame was extracted automatically using the mean gray value method from hyperspectral images. Then, the image power of ROI was extracted as classification feature, which the spectral region covered 400¹000nm and contained94 wavelengths. At last, support vector data description ( SVDD) was used to develop the classification models for the insect-damaged edamame. In the validation set, the results indicated the automatic extracting ROI method based on the mean gray value achieved 100% accuracy for the normal samples, 75% accuracy for the insect-damaged samples, and 95.6% overall classification accuracy, which could discriminate insect-damaged edamame.