Automatic interpretation of otoliths using deep learning

Abstract

The age structure of a fish population has important implications for recruitment processes and population fluctuations, and is 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.\n\nAdvances 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.\n\nWe 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.\n\nAutomating this analysis will help to improve consistency, lower cost, and increase scale of age prediction. Similar approaches can likely be used for otoliths from other species as well as for reading fish scales. The method is therefore an important step forward for improving the age structure estimates of fish populations.