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Fishes
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Application of Artificial Intelligence in Aquaculture
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Application of Artificial Intelligence in Aquaculture

A special issue of Fishes (ISSN 2410-3888).

Deadline for manuscript submissions: 20 November 2025 | Viewed by 3345

Special Issue Editors

Dr. Hongwu Cui

E-Mail Website
Guest Editor
Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China
Interests: fish behavior recognition; fish appetite assessment; recirculating aquaculture; aquaculture wastewater treatment; denitrification; nitrogen removal
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Zhou

E-Mail Website
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
Dr. Ran Zhao

E-Mail Website
Guest Editor
National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China
Interests: computer vision; robotics; AI in smart fishery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of intelligent technology has brought about a huge transformation in the aquaculture industry. By integrating sensors, the Internet of Things, big data analysis, and artificial intelligence, intelligent aquaculture systems can achieve real-time monitoring of the breeding environment, precise management of feed distribution, timely early warning of disease risks, and optimization of breeding strategies. This leads to improved breeding efficiency, reduced costs, and reduced environmental impact.

To explore the extensive application of intelligent technology in the field of aquaculture, we are launching a Special Issue focusing on the “Application of Artificial Intelligence in Aquaculture”. We sincerely invite researchers around the world to submit the latest research results and practical cases on intelligent aquaculture, including, but not limited to, the following topics:

  • Intelligent environmental monitoring: Research on the use of sensors, the Internet of Things, and artificial intelligence technology to achieve real-time monitoring, prediction, and management of key environmental parameters such as water quality, temperature, and dissolved oxygen.
  • Disease early warning and management: Discuss the applications of intelligent technology in early diagnosis, early warning, and prevention and control of diseases, and to optimize disease management strategies through data analysis.
  • Optimization of aquaculture strategies: Based on the analysis of the growth of aquaculture organisms, environmental changes and market demand, explore the application of intelligent technology in the formulation of personalized aquaculture strategies.
  • Intelligent aquaculture and robotics: Explore the application of intelligent equipment and robotics in aquaculture, such as automatic feeding, fishing, and water quality management.
  • Environmental interaction and physiological response: Research on how the behavior and physiological expression of cultured organisms responds to changes in the intelligent feeding environment, as well as the mechanisms behind these changes.

Dr. Hongwu Cui
Dr. Chao Zhou
Dr. Ran Zhao
Guest Editors

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

  • aquaculture
  • fishery
  • fish behavior
  • intelligent recognition
  • deep learning
  • environmental monitoring
  • environmental interaction mechanisms

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

Published Papers (5 papers)

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Research

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13 pages, 262 KB  
Article
Optimizing Pellet Size and Feeding Strategy Using an Automatic Feeder in Juvenile Olive Flounder (Paralichthys olivaceus)
by Seong-Mok Jeong, Sang-Woo Hur, Jinho Bae and Byung Hwa Min
Fishes 2025, 10(9), 458; https://doi.org/10.3390/fishes10090458 - 11 Sep 2025
Viewed by 13
Abstract
Feeding is among the most labor-intensive tasks in aquaculture, yet it is critical for productivity and operational efficiency. Although automated feeding systems reduce labor, the absence of data on optimal pellet size, feeding rate, and frequency has led to inconsistent practices and productivity. [...] Read more.
Feeding is among the most labor-intensive tasks in aquaculture, yet it is critical for productivity and operational efficiency. Although automated feeding systems reduce labor, the absence of data on optimal pellet size, feeding rate, and frequency has led to inconsistent practices and productivity. We evaluated the applicability of a low-cost automatic feeder and determined optimal pellet size and feeding frequency for juvenile olive flounder (Paralichthys olivaceus) in an experiment where 600 fish (83.4 ± 0.7 g (mean ± SD)) were randomly assigned to 12 flow-through tanks (60 fish/tank) in triplicate. In Experiment 1, fish fed 5-mm pellets presented greater weight gain and protein efficiency ratio (PER) than those fed 3-mm pellets (p < 0.05). In Experiment 2, manual feeding was compared to automatic feeding. Fish fed three times per day at a 1.3% daily feed intake (DFI) achieved similar growth but an improved feed conversion ratio and PER compared to manually fed fish (p < 0.05). Our findings support the use of 5-mm pellets at 1.3% DFI with a low-cost automatic feeder for maintaining productive juvenile flounder. These results present a practical automation strategy that enables small and medium-sized aqua-farms to reduce labor while maintaining productivity equivalent to manual feeding. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Aquaculture)
19 pages, 1619 KB  
Article
Impact of Water Velocity on Litopenaeus vannamei Behavior Using ByteTrack-Based Multi-Object Tracking
by Jiahao Zhang, Lei Wang, Zhengguo Cui, Hao Li, Jianlei Chen, Yong Xu, Haixiang Zhao, Zhenming Huang, Keming Qu and Hongwu Cui
Fishes 2025, 10(8), 406; https://doi.org/10.3390/fishes10080406 - 14 Aug 2025
Viewed by 389
Abstract
In factory-controlled recirculating aquaculture systems, precise regulation of water velocity is crucial for optimizing shrimp feeding behavior and improving aquaculture efficiency. However, quantitative analysis of the impact of water velocity on shrimp behavior remains challenging. This study developed an innovative multi-objective behavioral analysis [...] Read more.
In factory-controlled recirculating aquaculture systems, precise regulation of water velocity is crucial for optimizing shrimp feeding behavior and improving aquaculture efficiency. However, quantitative analysis of the impact of water velocity on shrimp behavior remains challenging. This study developed an innovative multi-objective behavioral analysis framework integrating detection, tracking, and behavioral interpretation. Specifically, the YOLOv8 model was employed for precise shrimp detection, ByteTrack with a dual-threshold matching strategy ensured continuous individual trajectory tracking in complex water environments, and Kalman filtering corrected coordinate offsets caused by water refraction. Under typical recirculating aquaculture system conditions, three water circulation rates (2.0, 5.0, and 10.0 cycles/day) were established to simulate varying flow velocities. High-frequency imaging (30 fps) was used to simultaneously record and analyze the movement trajectories of Litopenaeus vannamei during feeding and non-feeding periods, from which two-dimensional behavioral parameters—velocity and turning angle—were extracted. Key experimental results indicated that water circulation rates significantly affected shrimp movement velocity but had no significant effect on turning angle. Importantly, under only the moderate circulation rate (5.0 cycles/day), the average movement velocity during feeding was significantly lower than during non-feeding periods (p < 0.05). This finding reveals that moderate water velocity constitutes a critical hydrodynamic window for eliciting specific feeding behavior in shrimp. These results provide core parameters for an intelligent Litopenaeus vannamei feeding intensity assessment model based on spatiotemporal graph convolutional networks and offer theoretically valuable and practically applicable guidance for optimizing hydrodynamics and formulating precision feeding strategies in recirculating aquaculture systems. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Aquaculture)
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15 pages, 1389 KB  
Article
Predicting the Body Weight of Tilapia Fingerlings from Images Using Computer Vision
by Lessandro do Carmo Lima, Adriano Carvalho Costa, Heyde Francielle do Carmo França, Alene Santos Souza, Gidélia Araújo Ferreira de Melo, Brenno Muller Vitorino, Vitória de Vasconcelos Kretschmer, Suzana Maria Loures de Oliveira Marcionilio, Rafael Vilhena Reis Neto, Pedro Henrique Viadanna, Gabriel Rinaldi Lattanzi, Luciana Maria da Silva and Kátia Aparecida de Pinho Costa
Fishes 2025, 10(8), 371; https://doi.org/10.3390/fishes10080371 - 2 Aug 2025
Viewed by 514
Abstract
The aim of this study was to develop a mathematical model to predict the body weight of tilapia fingerlings using variables obtained through computer vision. A total of 2092 tilapia fingerlings and juveniles, weighing between 10 and 100 g, were fasted for 12 [...] Read more.
The aim of this study was to develop a mathematical model to predict the body weight of tilapia fingerlings using variables obtained through computer vision. A total of 2092 tilapia fingerlings and juveniles, weighing between 10 and 100 g, were fasted for 12 h, anesthetized, weighed, and photographed using an iPhone 12 Pro Max at 33 cm height in a closed container with different bottom colors. Images were segmented using Roboflow’s instance segmentation model, achieving 99.5% mean average precision, 99.9% precision, and 100% recall. From the segmented images, area, perimeter, major axis (MA), minor axis (SA), X and Y centroids, compactness, eccentricity, and the MA/SA ratio were extracted. Seventy percent of the data was used to build the model, and 30% for validation. Stepwise multiple regression (backward selection) was performed, using body weight as the dependent variable. The prediction model was: −17.7677 + 0.0007539(area) – 0.0848303 (MA) – 0.108338(SA) + 0.0034496(CX). The validation model showed similar coefficients and R2 = 0.99. The second validation, using observed versus predicted values, also yielded an R2 of 0.99 and a mean absolute error of 1.57 g. Correlation and principal component analyses revealed strong positive associations among body weight, area, axes, and predicted values. Computer vision proved effective for predicting tilapia fingerlings’ weight. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Aquaculture)
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25 pages, 13877 KB  
Article
Fine-Grained Fish Individual Recognition in Underwater Environments Using Global Detail Enhancement and Keypoint Region Fusion
by Yidan Zhao, Ming Chen, Guofu Feng, Wanying Zhai, Peng Xiao and Yongxiang Huang
Fishes 2025, 10(3), 102; https://doi.org/10.3390/fishes10030102 - 27 Feb 2025
Cited by 1 | Viewed by 693
Abstract
With the rapid advancement of intelligent aquaculture, precise individual identification of underwater fish has become a crucial method for achieving smart farming. By accurately recognizing and tracking individuals within the same species, researchers can enable individual-level identification and tracking, significantly enhancing the efficiency [...] Read more.
With the rapid advancement of intelligent aquaculture, precise individual identification of underwater fish has become a crucial method for achieving smart farming. By accurately recognizing and tracking individuals within the same species, researchers can enable individual-level identification and tracking, significantly enhancing the efficiency of research and management. To address the challenges of complex underwater environments and subtle differences among similar individuals that affect recognition accuracy, this paper proposes a fish individual identification method based on global detail enhancement and keypoint region fusion. This method simultaneously learns global refined features and keypoint region features, dynamically capturing effective keypoint features while mitigating errors caused by noise through weighted fusion. The network first employs a global detail enhancement module to extract global features, such as overall morphology and texture information, followed by the extraction of fine-grained features from keypoint regions. Through the weighted fusion, the network further emphasizes critical areas, thereby enhancing robustness and adaptability in complex underwater scenarios. This design effectively integrates global refined features and local keypoint features, providing comprehensive support for accurate fish individual identification. Experimental results show that the proposed method achieves mAP and Rank-1 scores of 89.7% and 95.3%, respectively, and demonstrates strong generalization capabilities in other fish identification tasks. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Aquaculture)
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Review

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16 pages, 993 KB  
Review
The Application of Digital Twin Technology in the Development of Intelligent Aquaculture: Status and Opportunities
by Jianlei Chen, Yong Xu, Hao Li, Xinguo Zhao, Yang Su, Chunhao Qi, Keming Qu and Zhengguo Cui
Fishes 2025, 10(8), 363; https://doi.org/10.3390/fishes10080363 - 25 Jul 2025
Viewed by 889
Abstract
Aquaculture is vital for global food security but faces challenges like disease, water quality control, and resource optimization. Digital twin technology, a real-time virtual replica of physical aquaculture systems, emerges as a transformative solution. By integrating sensors and data analytics, it enables monitoring [...] Read more.
Aquaculture is vital for global food security but faces challenges like disease, water quality control, and resource optimization. Digital twin technology, a real-time virtual replica of physical aquaculture systems, emerges as a transformative solution. By integrating sensors and data analytics, it enables monitoring and optimization of water quality, feed efficiency, fish health, and operations. This review explores the current adoption status of digital twins in aquaculture, highlighting applications in real-time monitoring and system optimization. It addresses key implementation challenges, including data integration and scalability, and identifies emerging opportunities for advancing sustainable, intelligent aquaculture practices. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Aquaculture)
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