Non-invasive fish identification of individuals can provide new possibilities for the monitoring of fish cultivation, improve and make fish production technologies less demanding for farmers, and increase fish welfare. The aim of this research is to confirm the idea of automatic non-invasive image-based fish identification of individuals using visible features on a fish body and prove the pattern stability during the fish cultivation period. Visible patterns, such as black stripes along the body of a Sumatra barb (Puntigrus tetrazona), were used for machine identification of individual fish. Two experiments were completed: a short-term experiment (43 fish) to show the uniqueness of the stripe patterns for identification, and a long-term experiment (25 fish) to test the stability of patterns during the cultivation period. The overall accuracy of classification was 100% for data collection in one day and 88% between two data collection times. This study shows that visible patterns and image processing methods can be used to automatically identify individual fish of the same species. This is not just limited to Sumatra barb—the concept should work for any fish with unique visible skin patterns, for example, for commercial fish species like Atlantic salmon (Salmo salar) and European perch (Perca fluviatilis).
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Al-Jubouri Q, Al-Azawi RJ, Al-Taee M, Young I (2018) Efficient individual identification of zebrafish using Hue/Saturation/Value color model. Egypt J Aquat Res 44:271–277. https://doi.org/10.1016/j.ejar.2018.11.006
Cal L, Suarez-Bregua P, Cerdá-Reverter JM, Braasch I, Rotllant J (2017) Fish pigmentation and the melanocortin system. Comp Biochem Physiol -Part A Mol Integr Physiol 211:26–33. https://doi.org/10.1016/j.cbpa.2017.06.001
Carpentier AS, Jean C, Barret M, Chassagneux A, Ciccione S (2016) Stability of facial scale patterns on green sea turtles Chelonia mydas over time: a validation for the use of a photo-identification method. J Exp Mar Biol Ecol 476:15–21. https://doi.org/10.1016/j.jembe.2015.12.003
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. Proc - 2005 IEEE Comput Soc Conf Comput Vis Pattern Recognition, CVPR 2005 I:886–893. https://doi.org/10.1109/CVPR.2005.177
Díaz-Calafat J, Ribas-Marqués E, Jaume-Ramis S, Martínez-Nuñez S, Sharapova A, Pinya S (2018) Individual unique colour patterns of the pronotum of Rhynchophorus ferrugineus (Coleoptera: Curculionidae) allow for photographic identification methods (PIM). J Asia Pac Entomol 21:519–526. https://doi.org/10.1016/j.aspen.2018.03.002
Food and Agriculture Organization (2018) World fisheries and aquaculture Sofia report
Føre M, Frank K, Norton T, Svendsen E, Alfredsen JA, Dempster T, Eguiraun H, Watson W, Stahl A, Sunde LM, Schellewald C, Skøien KR, Alver MO, Berckmans D (2018) Precision fish farming: a new framework to improve production in aquaculture. Biosyst Eng 173:176–193. https://doi.org/10.1016/j.biosystemseng.2017.10.014
Hirsch PE, Eckmann R (2015) Individual identification of Eurasian perch Perca fluviatilis by means of their stripe patterns. Limnologica 54:1–4. https://doi.org/10.1016/j.limno.2015.07.003
Hsiao YH, Chen CC, Lin SI, Lin FP (2014) Real-world underwater fish recognition and identification, using sparse representation. Ecol Inform 23:13–21. https://doi.org/10.1016/j.ecoinf.2013.10.002
Huntingford FA, Borçato FL, Mesquita FO (2013) Identifying individual common carp Cyprinus carpio using scale pattern. J Fish Biol 83:1453–1458. https://doi.org/10.1111/jfb.12246
Kimmel R (1999) Demosaicing: Image reconstruction from color CCD samples. IEEE Trans Image Process 8:1221–1228. https://doi.org/10.1007/BFb0055693
Li W, Ji Z, Wang L, Sun C, Yang X (2017) Automatic individual identification of Holstein dairy cows using tailhead images. Comput Electron Agric 142:622–631. https://doi.org/10.1016/j.compag.2017.10.029
Navarro J, Perezgrueso A, Barría C, Coll M (2018) Photo-identification as a tool to study small-spotted catshark Scyliorhinus canicula. J Fish Biol 92:1657–1662. https://doi.org/10.1111/jfb.13609
Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987. https://doi.org/10.1109/TPAMI.2002.1017623
Ombredane D, Baglinière JL, Marchand F (1998) The effects of passive integrated transponder tags on survival and growth of juvenile brown trout (Salmo trutta L.) and their use for studying movement in a small river. Hydrobiologia 371:99–106. https://doi.org/10.1007/978-94-011-5090-3_12
Óscar M, Pep-Luis M, Sergio M et al (2015) APHIS: a new software for photo-matching in ecological studies. Ecol Inform 27:64–70. https://doi.org/10.1016/j.ecoinf.2015.03.003
Pautsina A, Císař P, Štys D, Terjesen BF, Espmark ÅMO (2015) Infrared reflection system for indoor 3D tracking of fish. Aquac Eng 69:7–17. https://doi.org/10.1016/j.aquaeng.2015.09.002
Pine WE, Pollock KH, Hightower JE et al (2003) Management quantitative decision analysis for sport fisheries management. Fisheries 8446. https://doi.org/10.1577/1548-8446(2003)28
Saitoh T, Shibata T, Miyazono T (2015) Image-based fish recognition. In: Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015. IEEE, pp 260–263
Shafait F, Mian A, Shortis M, Ghanem B, Culverhouse PF, Edgington D, Cline D, Ravanbakhsh M, Seager J, Harvey ES (2016) Fish identification from videos captured in uncontrolled underwater environments. ICES J Mar Sci J Cons 73:2737–2746. https://doi.org/10.1093/icesjms/fsw106
Šonka M, Hlavac V, Boyle R (2008) Image processing, analysis, and machine vision. (3d edition). Thomson. ISBN: 978-0-495-24438-7
Stien LH, Nilsson J, Bui S, Fosseidengen JE, Kristiansen TS, Øverli Ø, Folkedal O (2017) Consistent melanophore spot patterns allow long-term individual recognition of Atlantic salmon Salmo salar. J Fish Biol 91:1699–1712. https://doi.org/10.1111/jfb.13491
Strachan NJC, Nesvadba P, Allen AR (1990) Fish species recognition by shape analysis of images. Pattern Recogn 23:539–544. https://doi.org/10.1016/0031-3203(90)90074-U
Sugimoto M (2002) Morphological color changes in fish: regulation of pigment cell density and morphology. Microsc Res Tech 58:496–503. https://doi.org/10.1002/jemt.10168
Te Lai Y, Taskinen J, Kekäläinen J, Kortet R (2012) Non-invasive diagnosis for Philometra ovata (Nematoda) infection in the common minnow Phoxinus phoxinus. Appl Microbiol Biotechnol 93:2411–2418. https://doi.org/10.1007/s00436-012-3099-z
Villon S, Mouillot D, Chaumont M, Darling ES, Subsol G, Claverie T, Villéger S (2018) A Deep learning method for accurate and fast identification of coral reef fishes in underwater images. Ecol Inform 48:238–244, ISSN 1574-9541. https://doi.org/10.1016/j.ecoinf.2018.09.007
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The study was financially supported by the Ministry of Education, Youth and Sports of the Czech Republic – project, CENAKVA“(LM2018099) and the CENAKVA Centre Development [No.CZ.1.05/2.1.00/19.0380] and GAJU 013/2019/Z.
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Bekkozhayeva, D., Saberioon, M. & Cisar, P. Automatic individual non-invasive photo-identification of fish (Sumatra barb Puntigrus tetrazona) using visible patterns on a body. Aquacult Int 29, 1481–1493 (2021). https://doi.org/10.1007/s10499-021-00684-8