Feasibility of automating otolith ageing using CT scanning and machine learning / B.R. Moore, J. Maclaren, C. Peat, M. Anjomrouz, P.L. Horn, S. Hoyle.
By: Moore, B. R.
Contributor(s): McLaren, J | Peat, C | Anjomrouz, M | Horn, P. L. (Peter L.)
| Hoyle, S | Fisheries New Zealand (Government agency) [issuing body.]
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Material type: 
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WELLINGTON ONLINE | ELECTRONIC | 1 | Not for loan | 395720 |
"October 2019."
Archived by the National Library of New Zealand in PDF.
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.
ELECTRONIC
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