Potato blackheart disease is an internal defect, which decreases the quality and yield of potato processed products such as fries, chips and whole powder. At present, the classification of potatoes mainly focuses on their external quality, rather than internal defects. The purpose of this research was to develop a fast non-destructive detection technology that could be used to detect potato blackheart disease. A visible and near infrared (VIS-NIR) transmission spectroscopy platform was built for potato detection. The spectral transmission characteristics of healthy and blackheart potatoes were analyzed, and the spectral discrimination model parameters were further optimized. Based on the potato grading line and the PG2000 high-speed spectrometer, the transmission spectra of 470 potatoes, including 234 healthy potatoes and 236 blackheart potatoes, were collected using left-to-right transmission method, of which the light source and the optical fiber probe were located on the left and right sides of the fruit plate of grading line respectively. A partial least squares discriminant analysis (PLS-DA) model was established. Furthermore, the principal component analysis (PCA) and spectral morphological features were combined to select essential wavelengths for model optimization. According to the VIS-NIR transmission spectra, there were significant differences between healthy and blackheart potatoes in absorbance values and spectral morphological characteristics. The average spectral absorbance values of blackheart potatoes in the range of 650～900 nm were higher than that of healthy potatoes. The average spectrum curve of blackheart potatoes was relatively smooth without obvious absorption peaks. However, obvious absorption peaks around 665, 732 and 839 nm appeared in that of healthy potatoes. The average spectral difference of blackheart and healthy potatoes reached the maximum at 705 nm. Based on the PLS-DA method, a potato blackheart disease discrimination model was established, which had a significant effect on detecting blackheart disease. The area under the receiver operating characteristic curve (AUC), total discrimination accuracy, RMSECV and RMSEP of the model were 0.994 2, 97.16%, 0.28 and 0.26, respectively. Moreover, a useful wavelength combination consisting of 6 wavelengths (658, 705, 716, 800, 816 and 839 nm) was obtained. The total accuracy of the simplified model could reach 96.73%, which was similar to that of the full-band model. It is shown that the left-to-right transmission method can accurately and rapidly identify blackheart potatoes. The study provides an important theoretical, and practical basis for improving the online detection technology of internal potato defects.