Leveraging deep learning and computer vision technologies to enhance management of coastal fisheries in the Pacific region

Leveraging deep learning and computer vision technologies to enhance management of coastal fisheries in the Pacific region

This paper presents the design and development of a coastal fisheries monitoring system that harnesses artificial intelligence technologies. Application of the system across the Pacific region promises to revolutionize coastal fisheries management. The program is built on a centralized, cloud-based...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 20915 - 16
Main Authors: Shedrawi, George, Magron, Franck, Vigga, Bernard, Bosserelle, Pauline, Gislard, Sebastien, Halford, Andrew R., Tiitii, Sapeti, Fepuleai, Faasulu, Molai, Chris, Rota, Manibua, Jalam, Shivam, Fatongiatau, Viliami, Sami, Abel P., Nikiari, Beia, Sokach, Ada H. M., Joy, Lucy A., Li, Owen, Steenbergen, Dirk J., Andrew, Neil L.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 08-09-2024
Nature Publishing Group
Nature Portfolio
Subjects:
631/158/2458
631/158/672
704/829/826
Animals
Artificial Intelligence
Artisanal fisheries
Automation
Capacity development
Co-management
Coastal fisheries
Coastal fisheries management
Conservation of Natural Resources - methods
Data poor fisheries
Decision making
Deep Learning
Ecosystem
Environmental monitoring
Fish barriers
Fisheries
Fisheries - economics
Fisheries management
Fishery development
Fishes
Fishing
Fishing communities
Humanities and Social Sciences
Information processing
Monitoring
Monitoring systems
multidisciplinary
Pacific Ocean
Science
Science (multidisciplinary)
Pacific Ocean
Pacific region
Artisanal fisheries
Small Island Developing States
Data poor fisheries
Coastal fisheries management
Artificial intelligence
Monitoring
Co-management
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Summary:This paper presents the design and development of a coastal fisheries monitoring system that harnesses artificial intelligence technologies. Application of the system across the Pacific region promises to revolutionize coastal fisheries management. The program is built on a centralized, cloud-based monitoring system to automate data extraction and analysis processes. The system leverages YoloV4, OpenCV, and ResNet101 to extract information from images of fish and invertebrates collected as part of in-country monitoring programs overseen by national fisheries authorities. As of December 2023, the system has facilitated automated identification of over six hundred nearshore finfish species, and automated length and weight measurements of more than 80,000 specimens across the Pacific. The system integrates other key fisheries monitoring data such as catch rates, fishing locations and habitats, volumes, pricing, and market characteristics. The collection of these metrics supports much needed rapid fishery assessments. The system’s co-development with national fisheries authorities and the geographic extent of its application enables capacity development and broader local inclusion of fishing communities in fisheries management. In doing so, the system empowers fishers to work with fisheries authorities to enable data-informed decision-making for more effective adaptive fisheries management. The system overcomes historically entrenched technical and financial barriers in fisheries management in many Pacific island communities.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-71763-y