Abstract: Abstract: Potatos are one of the world's major food crops. It not only has medicinal value and food value, but also has industrial value. The quality of potatos is directly related to their commodity level, benefits, and market competitiveness. Therefore, its quality testing is an important part of potato processing. Currently, common non-destructive testing techniques (near infrared spectroscopy and machine vision technology) are unable to achieve simultaneous detection of a potato's internal and external quality. Transmission hyperspectral imaging technology has some penetrating ability, when the light passes through the agricultural products, spectral and image of hyperspectral imaging data will change according to the differences in their internal characteristics. Therefore, the transmission hyperspectral imaging technology not only can detect the internal quality of agricultural products, but also can detect some external qualities. Since the single detection technology cannot simultaneously detect the internal and external quality of potatoes, the internal black heart and external weight of potatoes are detected by the transmission hyperspectral imaging technology and fusing spectrum and image information. In this study, 266 hyperspectral images (400-1 000 nm) were collected by the transmission hyperspectral imaging system, and then the spectrum and the image information were extracted. Using a Monte Carlo cross-validation method to exclude the data of two abnormal black heart samples, and variable selection methods of uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to do the variable selection for the spectrum of the black heart sample. The eventual adoption of 9 spectral variables were used to establish the detection model of black heart by a partial least squares discriminant analysis (PLS-DA); variable selection methods of competitive adaptive reweighed sampling (CARS) and successive projections algorithm (SPA) were used to do variable selection for a weight testing sample spectrum, the eventual adoption of 9 variables established a detection model of weight testing by partial least-squares regression (PLS); the Area information of transmission hyperspectral image was extracted, which combined with the 9 spectral variables to set up an PLS model for weight detection based on spectral - image information. The research demonstrates that the accurate recognition rate of black heart is 100%, and the minimum shoddy area which could be identified was 1.88 cm2. The performance of the weight detection model based on the spectrum-image (10 variables) is much better than the one based on the spectrum (9 variables), the prediction correlation coefficient (Rp) was 0.99, and the forecast root mean square error (RMSEP) was 10.88. The results indicate that using the transmission hyperspectral imaging technology with the fusion of image and spectrum information to detect potatoes' internal black heart and external weight simultaneously is feasible.