Identifying losers: Automatic identification of growth-stunted salmon in aquaculture using computer vision

Author:

Banno KanaORCID,Gonçalves Filipe Marcel Fernandes,Sauphar Clara,Anichini Marianna,Hazelaar Aline,Sperre Linda Helen,Stolz Christian,Aas Grete Hansen,Gansel Lars Christian,da Silva Torres Ricardo

Funder

CNPq

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Norges Teknisk-Naturvitenskapelige Universitet

CAPES

Publisher

Elsevier BV

Reference69 articles.

1. A survey on fish classification techniques;Alsmadi;Journal of King Saud University - Computer and Information Sciences,2022

2. Banno, K., Gonçalves, F. M. F., Anichini, M., Gansel, L. C., & da Silva Torres, R. (2022). Automatic Detection of Growth-Stunted Phenotype in Farmed Atlantic Salmon: A New Insight into Quantify their Distribution and Bahaviour based on a Machine Learning Approach. In Aquaculture europe 2022 conference abstracts (pp. 70–71). URL https://eposters.blob.core.windows.net/eas-eposters/AE2022AbstractBook.pdf.

3. Healthy and loser salmon dataset;Banno,2024

4. A novel approach for wild fish monitoring at aquaculture sites: Wild fish presence analysis using computer vision;Banno;Aquaculture Environment Interactions,2022

5. Emerging indicators of fish welfare in aquaculture;Barreto;Reviews in Aquaculture,2022

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