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dc.contributor.authorMoen, Endre
dc.contributor.authorHandegard, Nils Olav
dc.contributor.authorAllken, Vaneeda
dc.contributor.authorAlbert, Ole Thomas
dc.contributor.authorHarbitz, Alf
dc.contributor.authorMalde, Ketil
dc.PublishedMoen E, Handegard NO, Allken V, Albert OT, Harbitz A, Malde K. Automatic interpretation of otoliths using deep learning. PLoS ONE. 2018;13(12):e0204713eng
dc.description.abstractThe 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 of determining age structure relies on manually reading age from otoliths, and the process is labor intensive and dependent on specialist expertise. Recent advances in machine learning have provided 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 pre-trained convolutional neural network designed for object recognition, to estimate the age of fish 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 documented precision obtained by human experts. Automating this analysis may help to improve consistency, lower cost, and increase the extent of age estimation. Given that adequate data are available, this method could also be used to estimate age of other species using images of otoliths or fish scales.en_US
dc.publisherPublic Library of Scienceen_US
dc.rightsAttribution CC BYeng
dc.titleAutomatic interpretation of otoliths using deep learningen_US
dc.typePeer reviewed
dc.typeJournal article
dc.rights.holderCopyright 2018 The Authorsen_US
dc.source.journalPLoS ONE

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