Citation: Bekkozhayeva, D.; Cisar, P.
Image-Based Automatic Individual
Identification of Fish without
Obvious Patterns on the Body (Scale
Pattern). Appl. Sci. 2022, 12, 5401.
https://doi.org/10.3390/
app12115401
Academic Editor: Peng-Yeng Yin
Received: 4 May 2022
Accepted: 24 May 2022
Published: 26 May 2022
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Article
Image-Based Automatic Individual Identification of Fish
without Obvious Patterns on the Body (Scale Pattern)
Dinara Bekkozhayeva * and Petr Cisar
Laboratory of Signal and Image Processing, Institute of Complex Systems, Faculty of Fisheries and Protection
of Waters, CENAKVA, University of South Bohemia in Ceske Budejovice, Zámek 136,
373 33 Nové Hrady, Czech Republic; cisar@frov.jcu.cz
* Correspondence: dbekkozhayeva@frov.jcu.cz; Tel.: +420-7-3178-0433
Abstract:
The precision fish farming concept has been widely investigated in research and is highly
desirable in aquaculture as it creates opportunities for precisely controlling and monitoring fish
cultivation processes and increasing fish welfare. The automatic identification of individual fish could
be one of the keys to enabling individual fish treatment. In a previous study, we already demonstrated
that the visible patterns on a fish’s body can be used for the non-invasive individual identification
of fishes from the same species (with obvious skin patterns, such as salmonids) over long-term
periods. The aim of this study was to verify the possibility of using fully-automatic non-invasive
photo-identification of individual fish based on natural marks on the fish’s body without any obvious
skin patterns. This approach is an alternative to stressful invasive tagging and marking techniques.
Scale patterns on the body and operculum, as well as lateral line shapes, were used as discriminative
features for the identification of individuals in a closed group of fish. We used two fish species:
the European seabass Dicentrarchus labrax and the common carp Cyprinus carpio. The identification
method was tested on four experimental data sets for each fish species: two separate short-term data
sets (pattern variability test) and two long-term data sets (pattern stability test) for European seabass
(300 individual fish) and common carp (32 individual fish). The accuracy of classification was 100%
for both fish species in both the short-term and long-term experiments. According to these results,
the methods used for automatic non-invasive image-based individual-fish identification can also be
used for fish species without obvious skin patterns.
Keywords:
precision fish farming; machine vision; individual-fish identification; non-invasive
identification; scale pattern; intensive aquaculture
1. Introduction
The use of automatization systems in aquaculture is not novel but is becoming increas-
ingly necessary to reduce human maintenance. The precision fish farming concept [
1
] is
based on automation processes, in which controlled-engineering principles are applied to
fish-production processes, which increases fish farmers’ abilities to control and monitor all
stages of fish cultivation. This process enables farmers to make data-based decisions. The
applications of automation in fish cultivation are very broad and include feeding control,
fish welfare and disease monitoring, fish sampling, and fish sorting.
Automatization has also been used in many biological research studies. For example,
new technologies (camera based system and automatized image processing) were success-
fully applied to fish behavior and welfare monitoring [
2
–
4
]. One main advantage of using
such novel technologies is fully automated data processing. For example, Zhou et al. [
5
]
developed an automatic monitoring system for feed consumption to maintain high-quality
water parameters for fish welfare. In such studies, it would be beneficial to obtain infor-
mation on individual fish, to monitor and maintain individual fish instead of fish groups.
Individual identification is a broad research area that is related to humans and other ani-
mals (e.g., cows and whales) [
6
,
7
]. Individual identification has also been applied to fish
Appl. Sci. 2022, 12, 5401. https://doi.org/10.3390/app12115401 https://www.mdpi.com/journal/applsci
