Development of deep learning approaches for automating age estimation of hoki and snapper / B.R. Moore, Z.T. A’mar, A.C.G. Schimel, C. Ó Maolagáin, S.D. Hoyle.

By: Moore, Bradley (Fisheries scientist).
Contributor(s): Fisheries New Zealand (Government agency).
Material type: materialTypeLabelBookSeries: New Zealand fisheries assessment report: 2021/69Publisher: Wellington, New Zealand : Fisheries New Zealand, Tini a Tangaroa, 2021Description: 1 online resource (33 pages).ISBN: 9781991019608.Subject(s): FISHERIES | NEW ZEALANDOnline resources: FAR 2021/69 Fisheries Infosite | NIWA document server Summary: This study used deep learning to provide an automatic estimation of age for hoki and snapper through a convolutional neural network (CNN). A reference library of otolith images from ~1060 hoki and 520 snapper was generated for use in the CNN. Results from models using these images suggest that deep learning has the potential to support the automation of fish ageing, although further research is required to build an operational tool useful for routine fish ageing.
List(s) this item appears in: New Zealand Fisheries Assessment Report
Tags from this library: No tags from this library for this title. Log in to add tags.
Holdings
Item type Current library Call number Copy number Status Date due Barcode
PDF PDF WELLINGTON
ONLINE
ONLINE 1 Not for loan 398988

"November 2021."

This study used deep learning to provide an automatic estimation of age for hoki and snapper through a convolutional neural network (CNN). A reference library of otolith images from ~1060 hoki and 520 snapper was generated for use in the CNN. Results from models using these images suggest that deep learning has the potential to support the automation of fish ageing, although further research is required to build an operational tool useful for routine fish ageing.

There are no comments on this title.

to post a comment.

Powered by Koha