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Fishes
Special Issues
Computer Vision Applications for Fisheries and Aquaculture
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Computer Vision Applications for Fisheries and Aquaculture

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: 31 July 2026 | Viewed by 1283

Special Issue Editors

Prof. Dr. Zhangying Ye

E-Mail Website
Guest Editor
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Interests: fishery facilities; mechanical equipment; information technology; intelligent aquaculture; welfare aquaculture
Dr. Chao Zhou

E-Mail Website
Co-Guest Editor
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Science, Beijing 100097, China
Interests: fish behavior and intelligent fish feeding; the welfare of aquatic animals in aquaculture; artificial intelligence and IoT in aquaculture
Special Issues, Collections and Topics in MDPI journals
Dr. Weiqiang Ni

E-Mail
Guest Editor Assistant
Fishery Machinery and Instrument Research Institute, Chinese Academy of Fisheries Sciences, Shanghai, China
Interests: fish behavior analysis; computer vision

Special Issue Information

Dear Colleagues,

The fisheries and aquaculture sectors are undergoing a transformative shift towards precision management and sustainable practices, driven by technological innovation. Computer vision, as a core component of artificial intelligence, is rapidly emerging as a powerful tool to address long-standing challenges in these fields. This Special Issue aims to compile high-quality research and review articles that showcase the latest advancements and applications of computer vision across the entire aquatic food production chain.

The scope encompasses, but is not limited to, the following areas: automated biomass estimation, individual fish identification and tracking, behavior analysis for welfare and feeding optimization, species classification and sorting, early disease detection, underwater habitat and seabed monitoring, quality assessment of harvested aquatic products (e.g., freshness grading, defect detection), and the development of novel imaging systems (e.g., underwater, hyperspectral, low-cost) tailored for aquatic environments. We seek contributions that not only demonstrate technical excellence in algorithm development (deep learning, image processing) but also clearly articulate their practical impact on improving efficiency, sustainability, and profitability in aquaculture operations and wild fisheries management.

Prof. Dr. Zhangying Ye
Dr. Chao Zhou
Guest Editors

Dr. Weiqiang Ni
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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

  • computer vision
  • aquaculture
  • fisheries management
  • underwater image analysis
  • fish behavior analysis
  • automated biomass estimation
  • species identification
  • quality inspection
  • precision aquaculture
  • deep learning

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

Published Papers (3 papers)

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Research

23 pages, 5389 KB  
Article
An Edge-Ready Lightweight Computer Vision Framework for On-Site Fish Disease Detection in Aquaculture Management
by Jiawen Li, Weidong Zhang, Shengzhao Xiao, Xuanzhong Chen, Yuesheng Huang, Jujian Lv, Kaihan Lin, Xianglei Hu, Xianxian Zeng and Rongjun Chen
Fishes 2026, 11(5), 280; https://doi.org/10.3390/fishes11050280 - 9 May 2026
Viewed by 176
Abstract
Efficient detection of fish diseases is essential for intelligent health monitoring and timely intervention in aquaculture. However, current computer vision models remain computationally intensive, hindering their deployment on resource-constrained edge devices in aquaculture applications. To this end, this study developed a lightweight detection [...] Read more.
Efficient detection of fish diseases is essential for intelligent health monitoring and timely intervention in aquaculture. However, current computer vision models remain computationally intensive, hindering their deployment on resource-constrained edge devices in aquaculture applications. To this end, this study developed a lightweight detection framework based on an improved You Only Look Once (YOLO), aiming to achieve a favorable balance between detection accuracy and on-site inference efficiency. First, a Dual-Branch Feature-Preserving Downsampling (DFPD) module was proposed to enhance the extraction of valuable disease-related cues with minimal computational overhead. Subsequently, structured pruning was applied to compress the optimized baseline model. Four pruning techniques, including Slim, GroupTaylor, Layer-Adaptive Magnitude-Based Pruning (LAMP), and L1-based, were evaluated under the same conditions. The enhanced baseline model improved precision from 0.864 to 0.908 and mAP@0.5:0.95 from 0.613 to 0.632, while already reducing the Number of Parameters (Params) and Giga Floating-point Operations Per Second (GFLOPs) compared with the original YOLOv8n. Among the pruning techniques, L1-based produced the best overall trade-off, yielding a final model that maintained a F1-score of 0.860 while reducing Params and GFLOPs by 54.7% and 49.4%, respectively, relative to the original detector. Ablation studies further revealed that a moderate FLOPs reduction of approximately 41% to 47% was optimal for preserving diagnostic performance while enhancing compactness. Edge deployment tests on an RK3588S device verified the framework’s practical inference speed advantage. Therefore, this study offers a deployment-friendly computer vision solution for on-site fish disease detection in aquaculture management, particularly suited to real-world scenarios with limited computational resources. Full article
(This article belongs to the Special Issue Computer Vision Applications for Fisheries and Aquaculture)
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29 pages, 31629 KB  
Article
Quantification of Opercular Pigmentation Changes in Farmed Atlantic Salmon: A Novel Application for Computer Vision in Fish Welfare Assessment
by Talha Laique, Mikkel Gunnes, Ole Folkedal, Jonatan Nilsson, Evelina A. L. Green, Hannah Normann Gundersen, Øyvind Øverli and Habib Ullah
Fishes 2026, 11(5), 271; https://doi.org/10.3390/fishes11050271 - 2 May 2026
Viewed by 356
Abstract
Intensive salmon farming is associated with high mortality rates, highlighting the need for new welfare indicators that can detect adverse conditions earlier and less invasively than many current approaches. Existing animal-based indicators used in the industry typically depend on subjective scoring and provide [...] Read more.
Intensive salmon farming is associated with high mortality rates, highlighting the need for new welfare indicators that can detect adverse conditions earlier and less invasively than many current approaches. Existing animal-based indicators used in the industry typically depend on subjective scoring and provide information mostly after welfare problems have already developed, thereby raising questions about their efficacy. Examples include emaciation, wounds, or scale loss, etc. Preliminary data and ongoing investigation suggest that melanin-based skin pigmentation may change dynamically with stress and condition in salmonid fishes. In this study, we present a semi-automated methodology for assessing changes in the grayscale intensity of melanin-based skin spots within the operculum region of adult Atlantic salmon (Salmo salar) kept in seawater. The pipeline combines computer vision models to detect the operculum, segment individual spots, and extract grayscale-based features for spot-level analysis over time. The method was applied to out-of-water images collected before and after exposure to a confinement episode. The results showed an overall shift in grayscale intensity from black to pigmentation fading after the challenge, although responses varied among individuals. These findings indicate that the proposed methodology can detect temporal changes in opercular melanin-based spots under applied experimental conditions. We therefore present this work as proof of principle for using computer vision to quantify changes in melanin-based skin spots as a potentially useful, non-invasive indicator of stress and welfare in Atlantic Salmon. Full article
(This article belongs to the Special Issue Computer Vision Applications for Fisheries and Aquaculture)
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19 pages, 2045 KB  
Article
Effects of Offshore Wind Farm-Associated Electromagnetic Fields on the Physiology and Behavior of Sebastes schlegelii
by Tingting Wen, Hongwu Cui, Zhengguo Cui, Xinxing Zhang, Qi Zhang, Juanjuan Sui, Xixi Han, Huanhuan Jiang, Congcong Xing, Mian Xie, Yanrong Zhou, Weihan Yin, Shengtao Chen and Qian Yang
Fishes 2026, 11(4), 243; https://doi.org/10.3390/fishes11040243 - 17 Apr 2026
Viewed by 332
Abstract
To evaluate the potential biological effects of electromagnetic fields from offshore wind farms on Sebastes schlegelii, a laboratory-controlled chronic exposure experiment was conducted using a magnet-based static magnetic field system. Each group contained 60 fish distributed across four replicate tanks, with 15 [...] Read more.
To evaluate the potential biological effects of electromagnetic fields from offshore wind farms on Sebastes schlegelii, a laboratory-controlled chronic exposure experiment was conducted using a magnet-based static magnetic field system. Each group contained 60 fish distributed across four replicate tanks, with 15 fish per tank, and the fish were continuously exposed for 20 d under controlled water-quality conditions. Daily video monitoring of collective shoaling behavior was combined with multi-tissue physiological and biochemical analyses. Electromagnetic field exposure increased the swimming speed, burst frequency, activity ratio, spatial coverage, occupancy entropy, and polarization, while reducing the nearest neighbor distance, group radius, and group area. At the physiological level, cortisol increased mainly in the liver and brain, ACTH showed tissue-dependent modulation, SOD remained relatively stable, and glutathione increased in multiple tissues, especially in the liver, gut, and brain. Correlation analysis indicated a close coupling between behavioral reorganization and endocrine–redox regulation, suggesting that chronic EMF exposure shifted Sebastes schlegelii into a stress-associated but functionally coordinated collective state. Full article
(This article belongs to the Special Issue Computer Vision Applications for Fisheries and Aquaculture)
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