ArticleJournal of the Royal Society of New Zealand2023
Computer vision in aquaculture: a case study of juvenile fish counting.
Article in Journal of the Royal Society of New Zealand, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Gene Expression-Based Classification of European Seabass Larval Batches According to Saddleback Syndrome Incidence Using Machine Learning.Animals : an open access journal from MDPI · 2026Article
- From manual counting to YOLO: Using computer vision to automate large-scale fecundity assays in C. elegans.PloS one · 2026Article
- Advancing genetic improvement in the omics era: status and priorities for United States aquaculture.BMC genomics · 2025Review
- Identification and Counting of PirapitingaAnimals : an open access journal from MDPI · 2024Article
- Artificial Intelligence in New Zealand: applications and innovation.Journal of the Royal Society of New Zealand · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In aquaculture breeding or production programmes, counting juvenile fish represents a considerable cost in terms of the human hours needed. In this study, we explored the use of two state-of-the-art machine learning architectures (Single Shot Detection, hereafter SSD and Faster Regions with convolutional neural networks, hereafter Faster R-CNN) to augment a manual image-based juvenile fish counting method for the Australasian snapper (
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