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Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach

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Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach

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dc.contributor.author Muñoz-Benavent, Pau es_ES
dc.contributor.author Andreu García, Gabriela es_ES
dc.contributor.author Martínez-Peiró, Joaquín es_ES
dc.contributor.author Puig Pons, Vicente es_ES
dc.contributor.author Morillo-Faro, Andrés es_ES
dc.contributor.author Ordoñez-Cebrian, Patricia es_ES
dc.contributor.author Atienza-Vanacloig, Vicente es_ES
dc.contributor.author Pérez Arjona, Isabel es_ES
dc.contributor.author Espinosa Roselló, Víctor es_ES
dc.contributor.author Alemany, Francisco es_ES
dc.date.accessioned 2024-09-02T18:00:07Z
dc.date.available 2024-09-02T18:00:07Z
dc.date.issued 2024-02 es_ES
dc.identifier.uri http://hdl.handle.net/10251/207177
dc.description.abstract [EN] In this article, the evolution of BFT (bluefin tuna) sizes in fattening cages is studied, for which it was necessary to perform exhaustive monitoring with stereoscopic cameras and an exhaustive analysis of the data using automatic procedures. Exploring the size evolution of BFT over a long period is an important step in inferring their growth patterns, which are essential for designing smart aquaculture and sustainable fishing, and even assessing their health status. An important objective of this work was to verify whether tuna in captivity, in addition to fattening, grow in length. To this end, our autonomous monitoring system, equipped with stereoscopic cameras, was installed from 28 July 2020 to 23 May 2021 in a fattening cage in the Mediterranean containing 724 free-swimming tuna. This system provides thousands of images that, grouped by time intervals, allow us to conduct our studies. An automatic procedure, already introduced in a previous work and capable of processing large volumes of data, is used to estimate the length and width of individuals in ventral stereoscopic images of fish, and the evolution over time is analysed for each biometric characteristic. However, verifying the evolution of length and width based only on means or medians of these measurements may be inconsistent and insufficiently accurate to support our study objectives, as individuals of different sizes and ages may grow at different rates. Therefore, a modal analysis (Bhattacharya's method) was undertaken to identify the cohorts within the population. The results showed that each modal length surpassed the length of the next cohort and that there was accelerated growth in cages compared to the wild. In addition, we proved that using a length-width-weight relationship to estimate fish weight gives more accurate results than traditional length-weight relationships for fish fattened in cages. es_ES
dc.description.sponsorship This study forms part of the ThinkInAzul programme and was supported by the MCINwith funding from the European Union's NextGenerationEU (PRTR-C17.I1) and the Generalitat Valenciana (THINKINAZUL/2021/007, THINKINAZUL/2021/009, and AICO/2021/016). This work was carried out under the ICCAT Atlantic-Wide Research Programme for Bluefin Tuna (ICCATGBYP 10/2020), which is funded by the European Union, several ICCAT CPCs, the ICCAT Secretariat, and other entities (see https://www.iccat.int/gbyp/en/overview.asp, accessed on 22 January 2024). es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Fishes es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Bluefin tuna growth es_ES
dc.subject Fish monitoring es_ES
dc.subject Fish weight estimation es_ES
dc.subject Stereoscopic computer vision es_ES
dc.subject Cohort-based approach es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/fishes9020046 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GV INNOV.UNI.CIENCIA//THINKINAZUL%2F2021%2F007//INTEGRACION DE TECNOLOGIA DIGITAL Y DEEP LEARNING PARA CONTRIBUIR A MODELOS DE PESCA Y ACUICULTURA INTELIGENTES, MEDIANTE PROCESAMIENTO AUTOMÁTICO DE IMAGENES (ACUINTTEC)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GV INNOV.UNI.CIENCIA//THINKINAZUL%2F2021%2F009//Monitorización acústica para una acuicultura de precisión: red de observación acústica en granjas marinas mediterráneas (ACUPREC)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//AICO%2F2021%2F016//TECNICAS AVANZADAS DE VXC BASADAS EN DEEP LEARNING Y CNNS PARA LA CARACTERIZACION BIOMETRICA DEL ATUN ROJO/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//PRTR-C17.I1/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Politécnica Superior de Alcoy - Escola Politècnica Superior d'Alcoi es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Muñoz-Benavent, P.; Andreu García, G.; Martínez-Peiró, J.; Puig Pons, V.; Morillo-Faro, A.; Ordoñez-Cebrian, P.; Atienza-Vanacloig, V.... (2024). Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach. Fishes. 9(2). https://doi.org/10.3390/fishes9020046 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/fishes9020046 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 2 es_ES
dc.identifier.eissn 2410-3888 es_ES
dc.relation.pasarela S\511353 es_ES
dc.contributor.funder GENERALITAT VALENCIANA es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder International Commission for the Conservation of Atlantic Tunas es_ES


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