Abstract
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.
Highlights
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 study was large because 328 fish were used to test the feasibility of using the lateral skin dot pattern of Atlantic salmon for individual identification and a 6 month period of fish growth was used to study the pattern stability during fish cultivation
This study examined the possibility of the automatic individual identification of Atlantic salmon over 6 months of cultivation
Summary
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. A few studies have shown that fish body patterns can be used for individual identification, but no system for the automation of this exists. Several s tudies[17,18,19,20,21,22,23], exist on the non-invasive individual identification of fish using images but only few of them use machine vision. The majority of the studies use human experts for identification based on the fish images[20,22]. These studies proved that fish appearance based individual identification is feasible for the carp (Ciprinus carpio) (15 fish, 95.76% accuracy)[20] and catshark (Scyliorhinus canicular) (25 fish, 99.6% accuracy)[22] in the short-term. A long-term study of wild populations of cutthroat trout[21], in which datasets from 1997 and 1999 were compared, showed that two Scientific Reports | (2021) 11:16904
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