Automatic interpretation of otoliths using deep learning

The age structure of a fish population has important implications for
recruitment processes and population fluctuations, and is a key input to
fisheries assessment models. The current method relies on manually
reading age from otoliths, and the process is labor intensive and
dependent on specialist expertise.
Advances in machine learning have recently brought forth methods that
have been remarkably successful in a variety of settings, with
potential to automate analysis that previously required manual
curation. Machine learning models have previously been successfully
applied to object recognition and similar image analysis tasks. Here
we investigate whether deep-learning models can also be used for
estimating the age of otoliths from images.
We adapt a standard neural network model designed for object
recognition to the task of estimating age from otolith images. The
model is trained and validated on a large collection of images
of Greenland halibut otoliths.
We show that the model works well, and that its precision is
comparable to, and may even surpass that, of human experts.
Automating this analysis will help to improve consistency, lower cost,
and increase scale of age estimation. Similar approaches can likely
be used for otoliths from other species as well as for reading fish
scales. This method can likely be applied to the otoliths of other
species, as well as to fish scales.