Automated morphometry and weight prediction of juvenile Chinook Salmon leveraging open-source deep learning models

Abstract

Minimizing handling of threatened and endangered fish has become increasingly important as populations have dwindled. To minimize handling in morphometric measurements, the HandsFreeFishing program has been developed for juvenile Chinook Salmon (Oncorhynchus tshawytscha). By segmenting a 2D image many morphometric measurements are able to be estimated; from these measurements a weight prediction model is built based on fish whose ground truthed weights were measured using a digital scale. While many segmentation methods may be used, here Metas Segment Anything model (SAM) is employed to produce segmentation masks of raw images. This model is open-source and easily used on any image (of any size) with good performance. In the proposed framework, the user supplies a bounding box around a target fish along with minimal orientation data (left or right facing, upside down or right-side up); the rest of the segmentation, feature extraction, and final weight prediction is completely automated. A main goal of the segmentation is to estimate the surface area of the side profile of the fish. Then, assuming an ellipsoidal shape, this surface area can be related to the volume of the fish, which is directly proportional to the weight. Even on a relatively small dataset of 149 images (fork length 27-90mm) our results confirm the predictive qualities of the morphometric features measured. The model achieved weight prediction with a mean absolute error of 0.16 g with a mean absolute percentage error of 12%, and an r-squared value of 0.99, on fish ranging from 0.31g - 7.74g. The raw images come from a variety of fish viewers, the design of which is relatively inexpensive and reproducible, and, in conjunction with the HandsFreeFishing program, allows for minimal handling compared to traditional length and weight measurement methods.