Image segmentation is a crucial technique in the field of computer vision, particularly for underwater imagery, which poses unique challenges due to varying lighting conditions and water clarity. Embracing digital acceleration and social innovation in knowledge management, this study focuses on image segmentation for the detection of white spot disease in fish, a common and severe issue in aquaculture. The proposed method uses 4 segmentation techniques to segment infected regions on the fish’s body from underwater images. The segmented images are then analyzed to look for white spots, signs of the disease in question. That is why the possibility of early and highly accurate diagnosis of the disease using this approach will contribute to better management and prevention of diseases in aquaculture, and improvement of the health of fish stocks, and reduced losses accordingly. This study establishes the application of image segmentation as a useful feature in underwater disease detection systems and the application of advanced digital technologies and knowledge management in solving practical issues.