The quality of tomato products is significantly degraded due to defects on raw processing tomatoes such as insect hole or mildew. This research aims to investigate the potential of using visible/ near infrared (Vis/NIR) hyperspectral imaging for detection of insect hole and mildew on raw processing tomato. Tomato samples were imaged using a hyperspectral imaging system that covers a spectral range from 408 to 1013 nm. To images, region of interests (ROIs) were manually selected to extract mean spectra on every individual samples. Principal component analysis (PCA) was performed on the extracted spectra to select three optimal wavelengths (550, 750, 900 nm) for defects detection. PCA and pair-wise band ratio analysis were conducted on the spectral images using the optimal wavelengths to generate PC and band-ratio images, respectively. Masking, threshold-based segmentation, and morphologic operations were applied on the generated images to identify defective areas on the tomato surface. The accuracies of identifying insect hole, mildew, and healthy tomato achieved 93.3%, 90%, and 100% in the PC images, and 93.3%, 96.7%, and 100% in the band-ratio images, respectively. Therefore, the Vis-NIR hyperspectral imaging could be an effective approach for detecting insect hole and mildew on the surface of raw tomatoes. In addition, online detection system could be benefit by using the wavelengths of 550 nm and 750 nm.