Error statistics table.

In industrial societies, most fishery research institutes collect the phenotypic data of fish manually, which is time-consuming, labor-intensive, error-prone, and results in incomplete data. Considering their stress reaction and the natural body extension to collect the phenotypic data of fish quickly and accurately, channel catfish was used as the research subject and a deep-learning-based method was developed to explore their phenotypic data, i.e., body length, full length, head length, body height, tail handle width, tail handle height, and body thickness. First, this study applied two cameras and another device built into an image acquisition system to obtain images of fish in the water. We then adopted an Hourglass module network to position nine and ten key points on the top and side view images, building two key point fish skeletons. Finally, 3D coordinate transformation and scale parameters were employed to obtain the phenotypic data. Compared with the ground truth of the phenotypic fish data, our study achieved a 3.7% average relative error in terms of the full length, and an average 9.6% relative error for all seven types of phenotypic data applied. Furthermore, the average time required for the image processing measurements was approximately 1s.