AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishing

Domínguez Arca, Vicente; Ovalle Macías, Juan Carlos; Taboada Antelo, Luis

AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishing

This study introduces a transformative approach in longline fisheries, employing YOLO v7 object detection algorithm for real-time, automated sizing of hake. We have developed an artificial intelligence (AI) model based on Yolo v7 that classifies captured specimen of hake into four commercial size ca...

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Detalles Bibliográficos
Autores:	Domínguez Arca, Vicente, Ovalle Macías, Juan Carlos, Taboada Antelo, Luis
Tipo de recurso:	artículo
Fecha de publicación:	2024
País:	España
Institución:	Universitat Politècnica de Catalunya (UPC)
Repositorio:	UPCommons. Portal del coneixement obert de la UPC
Idioma:	inglés
OAI Identifier:	oai:upcommons.upc.edu:2117/411368
Acceso en línea:	https://hdl.handle.net/2117/411368 https://dx.doi.org/10.5821/iwp.2024.23.14123
Access Level:	acceso abierto
Palabra clave:	Fishery technology Hake Artificial intelligence Machine learning Longline fisheries Real-time fish inventory management YOLOv7 Tecnologia pesquera Lluç Àrees temàtiques de la UPC::Enginyeria agroalimentària::Pesca::Pesca marina Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic

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spelling	AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishingDomínguez Arca, VicenteOvalle Macías, Juan CarlosTaboada Antelo, LuisFishery technologyHakeArtificial intelligenceMachine learningLongline fisheriesReal-time fish inventory managementYOLOv7Tecnologia pesqueraLluçÀrees temàtiques de la UPC::Enginyeria agroalimentària::Pesca::Pesca marinaÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticThis study introduces a transformative approach in longline fisheries, employing YOLO v7 object detection algorithm for real-time, automated sizing of hake. We have developed an artificial intelligence (AI) model based on Yolo v7 that classifies captured specimen of hake into four commercial size categories, applicable to recorded or live-streaming video from on-board cameras in a new electronic monitoring (EM) system concept called iObserver Lite. This dual applicability demonstrates the model’s adaptability to different operational scenarios. Obtained results reveal the YOLO v7-based model’s outstanding accuracy in hake size detection, maintaining high precision in both controlled and real daily fishing activity environments. This performance is pivotal for real-time inventory management and offers the potential for advanced fishery analytics and real-time fish auction sales even before landing the catches. Moreover, by providing instantaneous catch size data, the technology aids in optimizing fishing efforts and supports sustainable fishing practices. The integration of YOLO v7 in longline fishing represents a significant technological leap, enhancing operational efficiency and contributing to achieving sustainable fishery management soon. This breakthrough showcases the vast potential of artificial vision and AI in revolutionizing the fishing industry, heralding a new era towards efficiency and sustainability of fishing activity regarding marine resource exploitation.Peer ReviewedSARTI20242024-01-0120242024-07-09journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/411368https://dx.doi.org/10.5821/iwp.2024.23.14123reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4113682026-05-27T15:37:01Z
dc.title.none.fl_str_mv	AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishing
title	AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishing
spellingShingle	AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishing Domínguez Arca, Vicente Fishery technology Hake Artificial intelligence Machine learning Longline fisheries Real-time fish inventory management YOLOv7 Tecnologia pesquera Lluç Àrees temàtiques de la UPC::Enginyeria agroalimentària::Pesca::Pesca marina Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
title_short	AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishing
title_full	AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishing
title_fullStr	AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishing
title_full_unstemmed	AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishing
title_sort	AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishing
dc.creator.none.fl_str_mv	Domínguez Arca, Vicente Ovalle Macías, Juan Carlos Taboada Antelo, Luis
author	Domínguez Arca, Vicente
author_facet	Domínguez Arca, Vicente Ovalle Macías, Juan Carlos Taboada Antelo, Luis
author_role	author
author2	Ovalle Macías, Juan Carlos Taboada Antelo, Luis
author2_role	author author
dc.subject.none.fl_str_mv	Fishery technology Hake Artificial intelligence Machine learning Longline fisheries Real-time fish inventory management YOLOv7 Tecnologia pesquera Lluç Àrees temàtiques de la UPC::Enginyeria agroalimentària::Pesca::Pesca marina Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
topic	Fishery technology Hake Artificial intelligence Machine learning Longline fisheries Real-time fish inventory management YOLOv7 Tecnologia pesquera Lluç Àrees temàtiques de la UPC::Enginyeria agroalimentària::Pesca::Pesca marina Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
description	This study introduces a transformative approach in longline fisheries, employing YOLO v7 object detection algorithm for real-time, automated sizing of hake. We have developed an artificial intelligence (AI) model based on Yolo v7 that classifies captured specimen of hake into four commercial size categories, applicable to recorded or live-streaming video from on-board cameras in a new electronic monitoring (EM) system concept called iObserver Lite. This dual applicability demonstrates the model’s adaptability to different operational scenarios. Obtained results reveal the YOLO v7-based model’s outstanding accuracy in hake size detection, maintaining high precision in both controlled and real daily fishing activity environments. This performance is pivotal for real-time inventory management and offers the potential for advanced fishery analytics and real-time fish auction sales even before landing the catches. Moreover, by providing instantaneous catch size data, the technology aids in optimizing fishing efforts and supports sustainable fishing practices. The integration of YOLO v7 in longline fishing represents a significant technological leap, enhancing operational efficiency and contributing to achieving sustainable fishery management soon. This breakthrough showcases the vast potential of artificial vision and AI in revolutionizing the fishing industry, heralding a new era towards efficiency and sustainability of fishing activity regarding marine resource exploitation.
publishDate	2024
dc.date.none.fl_str_mv	2024 2024-01-01 2024 2024-07-09
dc.type.none.fl_str_mv	journal article http://purl.org/coar/resource_type/c_6501 NA http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv	info:eu-repo/semantics/article
format	article
dc.identifier.none.fl_str_mv	https://hdl.handle.net/2117/411368 https://dx.doi.org/10.5821/iwp.2024.23.14123
url	https://hdl.handle.net/2117/411368 https://dx.doi.org/10.5821/iwp.2024.23.14123
dc.language.none.fl_str_mv	Inglés eng
language_invalid_str_mv	Inglés
language	eng
dc.rights.none.fl_str_mv	open access http://purl.org/coar/access_right/c_abf2 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	open access http://purl.org/coar/access_right/c_abf2 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv	openAccess
dc.format.none.fl_str_mv	application/pdf
dc.publisher.none.fl_str_mv	SARTI
publisher.none.fl_str_mv	SARTI
dc.source.none.fl_str_mv	reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC)
instname_str	Universitat Politècnica de Catalunya (UPC)
reponame_str	UPCommons. Portal del coneixement obert de la UPC
collection	UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score	15,811543

AI-driven hake catch monitoring: pioneering size-based inventory control in longline fishing

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