Fisheries Infosite

Feasibility of automating otolith ageing using CT scanning and machine learning


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FAR-2019-58-Automating-otolith-ageing.pdf (1.3 MB)

 This study explored using CT scanning and machine learning to automate fish ageing. CT scanning resolved banding patterns on the surface of snapper, hoki and ling otoliths, but resolution and contrast were too low to detect outer bands in images through otolith cores. A convolutional neural network was used to estimate snapper and hoki ages from otolith images, and after limited training obtained promising consistency with human ageing. Further development of both techniques is recommended.

Document date
Monday, 7 October 2019
Document type
V 1.3
File format
Adobe PDF
File size
1.3 MB
Reference number
FAR 2019/58
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Uploaded date
Monday, 7 October 2019

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AUTHOR: Moore, B.R.; Maclaren, J.; Peat, C.; Anjomrouz, M.; Horn, P.L.; Hoyle, S.;
ISBN: 978-1-99-000866-5;
ISSN: 1179-5352;
FAR: 2019/58;

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