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Fishes
Special Issues
New Technologies for Improving Fisheries and Aquaculture Production and Management
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New Technologies for Improving Fisheries and Aquaculture Production and Management

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: 30 September 2024 | Viewed by 1449

Special Issue Editor

Prof. Dr. Tsutomu Takagi

E-Mail Website
Guest Editor
Department of Fisheries Engineering, Hokkaido University, Sapporo, Japan
Interests: fisheries engineering; fish behavior; biomechanics; fluid dynamics; information science; control engineering

Special Issue Information

Dear Colleagues,

The production of fisheries and aquaculture is rising as the global demand for marine products increases. We are now at the stage of considering the development of new technologies to make the production and management of fisheries and aquaculture more sophisticated and efficient. The application of AI, which replaces work based on human experience and intuition with computers, is underway, but this may only be a superficial improvement in efficiency in various industrial fields, and the realization of DX (Digital Transformation) requires an understanding and clarification of the mechanisms and principles of essential phenomena. This concept is also necessary for DX in the fishery and aquaculture industries. This Special Issue aims to collate research results and review articles on new technologies and theories that lead to technological innovations and applications of conventional technologies to promote DX, which is necessary to advance production and management in the fishery and aquaculture industries. This Special Issue welcomes approaches which promote DX, especially from the perspectives of engineering, mathematical science, and information science.

Prof. Dr. Tsutomu Takagi
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

  • digital technology
  • new technology
  • DX
  • fisheries
  • aquaculture
  • engineering
  • mathematical science
  • information science
  • simulation technology
  • ICT

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

13 pages, 609 KiB  
Article
Enhancing Length at First Maturity Estimation Using Machine Learning for Fisheries Resource Management: A Case Study on Small Yellow Croaker (Larimichthys polyactis) in South Korea
by Heejoong Kang and Sang Chul Yoon
Fishes 2024, 9(10), 373; https://doi.org/10.3390/fishes9100373 - 24 Sep 2024
Abstract
Small yellow croaker (Larimichthys polyactis) is a critical economic fish species in South Korea, where effective management is essential due to concerns over declining populations. This study aims to enhance fishery management strategies by applying machine learning techniques to classify the [...] Read more.
Small yellow croaker (Larimichthys polyactis) is a critical economic fish species in South Korea, where effective management is essential due to concerns over declining populations. This study aims to enhance fishery management strategies by applying machine learning techniques to classify the maturity stages and estimate the length at first maturity (L50 and L95), comparing these results with those obtained using traditional macroscopic methods. Five machine learning models, including Decision Tree (DT), Random Forest (RF), LightGBM (LGBM), EXtreme Gradient Boosting (XGB) and Support Vector Machine (SVM), were developed and evaluated for their effectiveness in predicting maturity stages. The XGB model demonstrated superior performance with the highest evaluation final score and low computation time. Using generalized linear models (GLM), this study estimated L50 and L95 for both machine learning predictions and macroscopic observations. The results showed that machine learning models, particularly XGB, provided more precise estimates with narrower confidence intervals and better model fit than the traditional macroscopic methods. These findings can support more sustainable fisheries management practices by offering reliable tools for setting appropriate regulatory measures, such as minimum landing sizes, which contribute to the conservation of marine resources. Full article
(This article belongs to the Special Issue New Technologies for Improving Fisheries and Aquaculture Production and Management)
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14 pages, 2353 KiB  
Article
Quantitative Assessment and Analysis of Fish Behavior in Closed Systems Using Information Entropy
by Minoru Kadota, Shinsuke Torisawa and Tsutomu Takagi
Fishes 2024, 9(6), 224; https://doi.org/10.3390/fishes9060224 - 12 Jun 2024
Viewed by 581
Abstract
This study introduces a method for quantitatively assessing the complexity and predictability of fish behavior in closed systems through the application of information entropy, offering a novel lens through which to understand how fish adapt to environmental changes. Utilizing simulations rooted in a [...] Read more.
This study introduces a method for quantitatively assessing the complexity and predictability of fish behavior in closed systems through the application of information entropy, offering a novel lens through which to understand how fish adapt to environmental changes. Utilizing simulations rooted in a random walk model for fish movement, we delve into entropy fluctuations under varying environmental conditions, including responses to feeding and external stimuli. Our findings underscore the utility of information entropy in capturing the intricacies of fish behavior, particularly highlighting the synchrony in collective actions and adaptations to environmental shifts. This research not only broadens our comprehension of fish behavior but also paves the way for its application in fields like aquaculture and resource management. Through our analysis, we discovered that smaller grid sizes in simulations capture detailed local fluctuations, while larger grids elucidate general trends, pinpointing a 2.5 grid as optimal for our study. Moreover, changes in swimming speeds and behavioral adaptations during feeding were quantitatively analyzed, with results illustrating significant behavior modifications. Additionally, employing a Gaussian mixture model helped to clarify the nuanced changes in fish behavior in response to altered light conditions, demonstrating the layered complexity of fish responses to environmental stimuli. This investigation confirms the efficacy of information entropy as a robust metric for evaluating fish shoal behavior, offering a fresh methodology for ecological and environmental studies, with promising implications for sustainable management practices. Full article
(This article belongs to the Special Issue New Technologies for Improving Fisheries and Aquaculture Production and Management)
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