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Cited by 20Water Quality Monitoring TechnologiesSmart Agriculture and AIAdvanced Neural Network Applications

Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network

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Ienaga, Naoto, Higuchi, Kentaro, Takashi, Toshinori, Gen, Koichiro, Tsuda, Koji, Terayama, Kei·2021·Scientific Reports
DOI 10.1038/s41598-020-80001-0PMC PMC7804258
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Abstract

Closed-cycle aquaculture using hatchery produced seed stocks is vital to the sustainability of endangered species such as Pacific bluefin tuna (Thunnus orientalis) because this aquaculture system does not depend on aquaculture seeds collected from the wild. High egg quality promotes efficient aquaculture production by improving hatch rates and subsequent growth and survival of hatched larvae. In this study, we investigate the possibility of a simple, low-cost, and accurate egg quality prediction system based only on photographic images using deep neural networks. We photographed individual eggs immediately after spawning and assessed their qualities, i.e., whether they hatched normally and how many days larvae survived without feeding. The proposed system predicted normally hatching eggs with higher accuracy than human experts. It was also successful in predicting which eggs would produce longer-surviving larvae. We also analyzed the image aspects that contributed to the prediction to discover important egg features. Our results suggest the applicability of deep learning techniques to efficient egg quality prediction, and analysis of early developmental stages of development.

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  • High-quality eggs generally lead to higher hatching rates, improving recruitment success.

    “Consequently, high-quality eggs generally lead to higher hatching rates, improving the recruitment success (Kjørsvik et al., 2003; Ienaga et al., 2021).”

    From Impacts of phytoplankton availability on bigeye tuna (Thunnus obesus) recruitment in the Indian Ocean (2024) · cites this paper as Ienaga et al., 2021

    The Introduction (page unavailable) says low-quality eggs generally have poor hatching success and produce larvae with lower growth and survival, supporting the claim about egg quality and hatching. It links egg quality to efficient hatchery seed production, but does not establish an improvement in recruitment success. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Authors
Ienaga, N., Higuchi, K., Takashi, T., Gen, K., Tsuda, K., & Terayama, K.
Affiliations
  • Keio University (JP)
  • Japan Fisheries Research and Education Agency (JP)
  • National Institute for Materials Science (JP)
  • RIKEN (JP)
Corresponding author
Terayama, Kei
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2021-01-12
Volume
11
Issue
1
Pages
6-6
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-020-80001-0
PMC
PMC7804258
OpenAlex
W3120812961
Cited by
20
References
2
Citations by year
2026: 4 · 2025: 2 · 2024: 7 · 2023: 2 · 2022: 2 · 2021: 3
FWCI
1.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, other-oa)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Japan Society for the Promotion of Science (KAKEN 20K15587)
Topics
  • Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
Pacific bluefin tuna, Thunnus orientalis, deep neural network, larval survival, hatch rate, larval development, imaging features, endangered species conservation
MeSH terms
Animals, Humans, Image Processing, Computer-Assisted, Larva, Ovum, Quality Control, Tuna, Neural Networks, Computer, Aquaculture

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