Computer vision based individual fish identification using skin dot pattern
Autor: | Rudolf Schraml, Dinara Bekkozhayeva, Oleksandr Movchan, Petr Cisar, Mohammadmehdi Saberioon |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
0106 biological sciences
Computer science Fish farming Science Salmo salar Fish species Individualized treatment Skin Pigmentation 010603 evolutionary biology 01 natural sciences Article Pattern Recognition Automated Imaging Three-Dimensional Image processing Aquaculture Artificial Intelligence Animals 14. Life underwater Salmo Multidisciplinary biology business.industry 010604 marine biology & hydrobiology Pattern recognition biology.organism_classification Fully automatic Fish Medicine Identification (biology) Neural Networks Computer Artificial intelligence business Ichthyology |
Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021) Scientific Reports |
ISSN: | 2045-2322 |
Popis: | Precision fish farming is an emerging concept in aquaculture research and industry, which combines new technologies and data processing methods to enable data-based decision making in fish farming. The concept is based on the automated monitoring of fish, infrastructure, and the environment ideally by contactless methods. The identification of individual fish of the same species within the cultivated group is critical for individualized treatment, biomass estimation and fish state determination. A few studies have shown that fish body patterns can be used for individual identification, but no system for the automation of this exists. We introduced a methodology for fully automatic Atlantic salmon (Salmo salar) individual identification according to the dot patterns on the skin. The method was tested for 328 individuals, with identification accuracy of 100%. We also studied the long-term stability of the patterns (aging) for individual identification over a period of 6 months. The identification accuracy was 100% for 30 fish (out of water images). The methodology can be adapted to any fish species with dot skin patterns. We proved that the methodology can be used as a non-invasive substitute for invasive fish tagging. The non-invasive fish identification opens new posiblities to maintain the fish individually and not as a fish school which is impossible with current invasive fish tagging. |
Databáze: | OpenAIRE |
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