Table 1.

Description of terms used in this article

Name	Comment
10‐fold cross‐validation	With 10‐fold cross‐validation, the dataset is randomly split into 10 equal subsets. One subset is held out as the validation data, and a model is trained on the other 9 subsets. This procedure is repeated 10 times to evaluate each fold, and the results of each iteration are combined as the final estimate of performance.
composite image	The image produced by the FishLTM Recognition System. The composite image includes 18 images taken from 6 different cameras as a fish passes through the system. The composite image contains 9 images from color capture cameras and 9 images from near‐infrared (IR) cameras.
ensemble prediction	A prediction for length, girth, or weight of a fish that was obtained by individually passing the 9 color images of a composite image into a regressor and then averaging the output of the regressor for each image.
multi‐target regressor	A regressor that simultaneously predicts the length, girth, and weight of a fish from a single image.
single image	One of the 9 images in a composite image that was taken with one of the color capture cameras.
single‐model prediction	A prediction that is made using only one single image.
single‐target regressor	A regressor that only predicts one of length, girth, or weight.