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.

Laboratory of Signal and Image Processing Institute of Complex Systems FFPW CENAKVA University of South Bohemia in Ceske Budejovice Zámek 136 Nové Hrady 373 33 Czech Republic

Wavelab University of Salzburg Salzburg Austria

Zobrazit více v PubMed

Zamora-Izquierdo MA, Santa J, Martínez JA, Martínez V, Skarmeta AF. Smart farming IoT platform based on edge and cloud computing. Biosyst. Eng. 2019;177:4–17. doi: 10.1016/j.biosystemseng.2018.10.014. DOI

Saberioon M, Gholizadeh A, Cisar P, Pautsina A, Urban J. Application of machine vision systems in aquaculture with emphasis on Fish: State-of-the-Art and Key Issues. Rev. Aquacult. 2017;9(4):369–387. doi: 10.1111/raq.12143. DOI

Føre M, Frank K, Norton T, Svendsen E, Alfredsen JA, Dempster T, Eguiraune H, Watson W, Stahl A, Sunde LM, Schellewald C, Skøien KR, Alver M, Berckmans D. Precision fish farming: A new framework to improve production in aquaculture. Biosyst. Eng. 2018;173:176–193. doi: 10.1016/j.biosystemseng.2017.10.014. DOI

Saberioon M, Cisar P. Automated multiple fish tracking in three-dimension using a structured light sensor. Comput. Electron. Agric. 2016;121:215–221. doi: 10.1016/j.compag.2015.12.014. DOI

Yusup IM, Iqbal M, Jaya I. Real-time reef fishes identification using deep learning. IOP Conf. Ser. Earth Environ. Sci. 2020 doi: 10.1088/1755-1315/429/1/012046. DOI

Thorsteinsson, V. Tagging methods for stock assessment and research in fisheries. Report of Concerted Action FAIR CT.96.1394 (CATAG). Reykjavik. Marine Research Institute Technical Report (79), p. 179 (2002).

Bolland JD, Cowx IG, Lucas MC. Evaluation of VIE and PIT tagging methods for juvenile cyprinid fishes. J. Appl. Ichthyol. 2009;25:381–386. doi: 10.1111/j.1439-0426.2009.01261.x. DOI

McMahon TE, Dalbey SR, Ireland SC, Magee JP, Byorth PA. Management briefs: field evaluation of visible implant tag retention by brook trout, cutthroat trout, rainbow trout, and arctic grayling. North Am. J. Fish. Manag. 1996;16:921–925. doi: 10.1577/1548-8675(1996)016<0921:MBFEOV>2.3.CO;2. DOI

Gibbons WJ, Andrews KM. PIT tagging: Simple technology at its best. Bioscience. 2004;54(5):447–454. doi: 10.1641/0006-3568(2004)054[0447:PTSTAI]2.0.CO;2. DOI

Delcourt J, Ovidio M, Denoël M, Muller M, Pendeville H, Deneubourg J-L, Poncin P. Individual identification and marking techniques for zebrafish. Rev. Fish. Biol. Fish. 2018;28:839–864. doi: 10.1007/s11160-018-9537-y. DOI

Li W, Ji Z, Wang L, Sun C, Yang X. Automatic individual identification of Holstein dairy cows using tailhead images. Comput. Electron. Agric. 2017;142:622–631. doi: 10.1016/j.compag.2017.10.029. DOI

Whooley P, Berrow S, Barnes C. Photo-identification of fin whales (Balaenoptera physalus L.) off the south coast of Ireland. Mar. Biodivers. Rec. 2011;4:1–7. doi: 10.1017/S1755267210001119. DOI

Qin H, Li X, Liang J, Peng Y, Zhang C. DeepFish: Accurate underwater live fish recognition with a deep architecture. Neurocomputing. 2016;187:49–58. doi: 10.1016/j.neucom.2015.10.122. DOI

Lu Y, He X, Wen Y, Wang P. A new cow identification system based on iris analysis and recognition. Int. J. Biometrics. 2014;6:18–32. doi: 10.1016/j.compag.2017.10.029. DOI

Sawant MM, Bhurchandi KM. Age invariant face recognition: A survey on facial aging databases, techniques and effect of aging. Artif. Intell. Rev. 2019;52:981–1008. doi: 10.1007/s10462-018-9661-z. DOI

Villon S, Chaumont M, Subsol G, Villéger S, Claverie T, Mouillot D. Coral reef fish detection and recognition in underwater videos by supervised machine learning: comparison between deep learning and HOG+SVM methods. Lecture Notes in Computer Science book series LNCS, volume 10016. 2016 doi: 10.1007/978-3-319-48680-2_15. DOI

Dala-Corte RB, Moschetta JB, Becker FG. Photo-identification as a technique for recognition of individual fish: A test with the freshwater armored catfish. Neotrop. Ichthyol. 2016 doi: 10.1590/1982-0224-20150074. DOI

Stien LH, Nilsson J, Bui S, Fosseidengen JE, Kristiansen TS, Øverli Ø, Folkedal O. Consistent melanophore spot patterns allow long-term individual recognition of Atlantic salmon Salmo salar. J. Fish. Biol. 2017;91:1699–1712. doi: 10.1111/jfb.13491. PubMed DOI

Al-Jubouri Q, Al-Azawi RJ, Al-Taee M, Young I. Efficient individual identification of zebrafish using Hue/Saturation/Value color model. Egypt. J. Aquat. Res. 2018;44:271–277. doi: 10.1016/j.ejar.2018.11.006. DOI

Navarro J, Perezgrueso A, Barría C, Coll M. Photo-identification as a tool to study small-spotted catshark Scyliorhinus canicular. J. Fish Biol. 2018;92:1657–1662. doi: 10.1111/jfb.13609. PubMed DOI

Gifford S. M. & Mayhood, D. Natural marks for identifying individual fish in small populations of at-risk westslope cutthroat trout. Wild Trout IX: Sustaining wild trout in a changing world. At: Bozeman, MTVolume: Wild Trout Symposium XI (2014).

Huntingford FA, Borçato FL, Mesquita FO. Identifying individual common carp Cyprinus carpio using scale pattern. J. Fish Biol. 2013;83:1453–1458. doi: 10.1111/jfb.12246. PubMed DOI

Hirsch PE, Eckmann R. Individual identification of Eurasian perch Perca fluviatilis by means of their stripe patterns. Limnologica. 2015;54:1–4. doi: 10.1016/j.limno.2015.07.003. DOI

Chen Y, Medioni G. Object modelling by registration of multiple range images. Image Vision Computing. Butterworth-Heinemann. 1992;10(3):145–155. doi: 10.1109/ROBOT.1991.132043. DOI

Myronenko A, Song X. Point set registration: Coherent point drift. Proc. IEEE Trans. Pattern Anal. Mach. Intell. 2010;32(12):2262–2275. doi: 10.1109/TPAMI.2010.46. PubMed DOI

Dalal N, Triggs B. Histograms of oriented gradients for human detection. Comput. Vis. Pattern Recognit. 2005 doi: 10.1109/CVPR.2005.177. DOI

Føre M, Franka K, Dempster T, Alfredsen JA, Høyad E. Biomonitoring using tagged sentinel fish and acoustic telemetry in commercial salmon aquaculture: A feasibility study. Aquacul. Eng. 2017;78(Part B):163–172. doi: 10.1016/j.aquaeng.2017.07.004. DOI

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. 2021 doi: 10.1007/s10499-021-00684-8. DOI

Schellewald, A., Stahl, O., Marković, M., Marković, I., Hammerset, E., Moen, H., Trengereid & L. M., Sunde. Towards an individual characterization of Farmed Salmon. In Proceedings of Aquaculture Europe 2019 (2019).

Data for non-invasive (photo) individual fish identification of multiple species