Management and Technology for Tuna Fisheries

A special issue of Fishes (ISSN 2410-3888). This special issue belongs to the section "Fishery Facilities, Equipment, and Information Technology".

Deadline for manuscript submissions: 10 May 2025 | Viewed by 1859

Special Issue Editor


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Guest Editor
College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, China
Interests: fishing gear numerical simulation; fishing gear mechanics; fishing gear design; development of fishing equipment systems; machine learning models; fishing ground forecast; tuna fishery management

Special Issue Information

Dear Colleagues,

Tuna species represent a valuable food source for millions of people and are fundamental within the global economy. Tuna also comprise a key aspect of the health of the greater marine ecosystem. We are currently at a stage where we are considering the development of new technologies to make the production and management of tuna fisheries more sophisticated and efficient. The application of AI means that work that has previously been dependent on human experience and intuition can instead be performed by computers. Incorporating digital twins requires an understanding and clarification of the mechanisms and principles behind essential phenomena in tuna fisheries. Moreover, we must be sure to follow best practices in terms of sustainable management approaches.

This Special Issue will collate research results and review articles on new technologies, theories, and best practices in order to achieve technological innovations, including the application of digital twins, which are necessary for advancements in production and management in tuna fisheries. For this Special Issue, we welcome approaches that promote the use of digital twins and best practices for managing sustainable tuna fisheries, especially within the fields of engineering, mathematical science, information science, and remote sensing technology. Topics of interest include, but are not limited to, the following:

  • Tuna behavior;
  • Tuna population distribution;
  • Fishing gear numerical simulations, mechanics, and design;
  • Best practices to mitigate the impacts of tuna fisheries on ecosystems;
  • Applications of new technologies in fishing equipment systems;
  • Tuna fishery management.

Prof. Dr. Liming Song
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fishes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fishing gear numerical simulation
  • fishing gear mechanics
  • fishing gear design
  • development of fishing equipment systems
  • machine learning models
  • fishing ground forecast
  • tuna fishery management

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Published Papers (1 paper)

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Research

24 pages, 5201 KiB  
Article
A Deep Learning Approach to Automated Treatment Classification in Tuna Processing: Enhancing Quality Control in Indonesian Fisheries
by Johan Marcus Tupan, Fredrik Rieuwpassa, Beni Setha, Wilma Latuny and Samuel Goesniady
Fishes 2025, 10(2), 75; https://doi.org/10.3390/fishes10020075 - 13 Feb 2025
Cited by 1 | Viewed by 1590
Abstract
The Indonesian maritime territory harbors a rich diversity of marine resources, making up approximately 37% of global fish species diversity. Tuna, particularly in Maluku Province, stands out as a vital economic asset with growing production and export numbers. Current practices for processing and [...] Read more.
The Indonesian maritime territory harbors a rich diversity of marine resources, making up approximately 37% of global fish species diversity. Tuna, particularly in Maluku Province, stands out as a vital economic asset with growing production and export numbers. Current practices for processing and evaluating tuna meat, however, face significant limitations due to basic infrastructure and reliance on manual inspection methods, leading to potential contamination risks and treatment identification errors. This research addresses these challenges by implementing an advanced deep learning solution based on convolutional neural networks (CNNs) to automatically identify three distinct treatment categories for tuna loin: No-Treatment, CO-Treatment, and CS-Treatment. Trained on a comprehensive image dataset, the model demonstrated exceptional performance with 95% accuracy. While field testing confirmed the model’s strong performance in correctly identifying treatment categories, occasional classification errors highlighted areas for improvement in data preprocessing. This study provides a significant step forward in automated fish processing assessment technology, offering a promising solution to longstanding challenges in the marine processing industry. Full article
(This article belongs to the Special Issue Management and Technology for Tuna Fisheries)
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