Automatic interpretation of otoliths using deep learning

• Main Author: Moen, Endre
• Format: Dataset
• Language: unknown
• Published: Institute of Marine Research, Bergen, Norway 2018
• Subjects: Greenland
• Online Access: https://dx.doi.org/10.21335/nmdc-1949633559; http://metadata.nmdc.no/metadata-api/landingpage/a3e59fb0e340a4f4a7ba76ea658b64b8

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.