Integrating deep learning, biological hierarchies, and high-resolution imagery to create a new identification tool for cryptic coral reef fishes.

Reginato LF, Brandl SJ.

Open source

DOI: 10.1371/journal.pone.0349646
Published: 2026-06-04
Container: PLoS One
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0349646,
  title = {Integrating deep learning, biological hierarchies, and high-resolution imagery to create a new identification tool for cryptic coral reef fishes.},
  author = {Reginato LF and  Brandl SJ.},
  year = {2026},
  journal = {PLoS One},
  doi = {10.1371/journal.pone.0349646},
  url = {https://doi.org/10.1371/journal.pone.0349646}
}

RIS

TY  - JOUR
TI  - Integrating deep learning, biological hierarchies, and high-resolution imagery to create a new identification tool for cryptic coral reef fishes.
AU  - Reginato LF
AU  -  Brandl SJ.
PY  - 2026
JO  - PLoS One
DO  - 10.1371/journal.pone.0349646
UR  - https://doi.org/10.1371/journal.pone.0349646
ER  -

APA

LF, R., & SJ., B. (2026). Integrating deep learning, biological hierarchies, and high-resolution imagery to create a new identification tool for cryptic coral reef fishes.. PLoS One. https://doi.org/10.1371/journal.pone.0349646

Source records

europe-pmc · retrieved 2026-09-26T02:05:44.166Z
doaj · retrieved 2026-09-26T02:05:44.198Z