Abstract: The common scab is a skin disease of the potato tuber that decreases the quality of the product and significantly influences the price, so it is very necessary to find a quickly nondestructive way to detect potato scabs. In this study, machine vision technology and near infrared spectroscopy analysis technology were used to detect potato scabs. In order to improve the potato scab nondestructive recognition accuracy, multi-sensor information fusion technique was proposed to detect potato scabs based on machine vision and near infrared spectroscopy. DS evidence theory combined with support vector machine method was used for multi-sensor information fusion technique. In the research, 360 potatoes were taken as testing samples (180 qualified potatoes and 180 scab potatoes). This study concluded that the difference image method combined with the Markov random field model method was the best segmentation method in the segmentation of image characteristics through the image preprocessing. And the principal component analysis method was the best method in the spectral feature extraction through the spectroscopy preprocessing. This study compared several different spectral preprocessing methods to preprocess the near infrared spectroscopy in near infrared spectroscopy preprocessing. And from the discriminating rate of the support vector machine model with the pretreated near infrared spectroscopy, it was concluded that the dimension reduction method was the best spectroscopy preprocessing method. The support vector machine method was a good pattern recognition method, so this study used the support vector machine method to detect potato scabs based on machine vision technology and near infrared spectroscopy analysis technology. The support vector machine models to discriminate potato scab were built based on machine vision technology and near infrared spectroscopy analysis technology respectively. The discriminating rates of these two models were 89.17% and 91.67% in testing sets respectively. To improve the discriminating rates of potato scab detecting with machine vision and near infrared spectroscopy respectively, a multi-sensor information fusion technique based on near infrared spectroscopy and machine vision method was used to detect the potato scab. DS evidence theory was a good information fusion method, so DS evidence theory combined with support vector machine method model was built with image characteristics and spectral characteristics. The multi-sensor information fusion model was used to detect the testing potato samples and the discriminating rates were 95.83% in the testing set. Compared with the results from the three detecting models, it was concluded that the discriminating rate of the model built with multi-sensor information fusion was 6.66% higher than the model built with machine vision technology, and 4.16% higher than the model built with near infrared spectroscopy analysis technology. The results indicated that it was feasible to detect potato scabs by using a multi-sensor information fusion technique based on near infrared spectroscopy and machine vision. The recognizing rate of the multi-sensor information fusion model was higher than that of the model built by machine vision technology or near infrared spectroscopy analysis technology respectively. That is to say multi-sensor information fusion technology is better for potato scab nondestructive detecting than machine vision technology respectively or near infrared spectroscopy analysis technology respectively. The research can provide references for potato disease detecting with a multi-sensor information fusion technique.