Fishes

Construction and Application of Big Data Platform and Model for the Detection and Warning of Aquatic Diseases

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Special Issue Information
Keywords
Published Papers

A special issue of Fishes (ISSN 2410-3888). This special issue belongs to the section "Fish Pathology and Parasitology".

Deadline for manuscript submissions: closed (16 April 2024) | Viewed by 631

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Special Issue Editors

Prof. Dr. Fei Yin

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Guest Editor

Department of Aquaculture, School of Marine Sciences, Ningbo University, Ningbo 315832, China
Interests: aquaculture; aquatic animal medicine; smart fisheries

Dr. Azmi Al-Jubury

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Guest Editor

Faculty of Health and Medical Sciences, University of Copenhagen, 1870 Frederiksberg C, Denmark
Interests: biology of the aquatic diseases; water born zoonotic diseases; the development of novel treatment of diseases

Dr. Xiao Xie

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Guest Editor

Department of Aquaculture, School of Marine Sciences, Ningbo University, Ningbo 315832, China
Interests: prevention and control of aquatic diseases

Special Issue Information

Dear Colleagues,

In recent years, diseases have occurred in the cultivation of aquatic animals. For decades, scientific researchers have conducted exploration and experiments; however, in actual fish farming production, diseases remain a significant problem. Each method is established under limited conditions, but in actual application, they need to be adjusted in real time due to environmental and other conditions, which is a great challenge for farmers. Having a generalizable and easy-to-grasp intelligent decision-making platform is thus highly desirable.

In recent years, the development of computational and intelligent technology has made it easier for people to store and process the data obtained during aquaculture. The data originate from various sources, including historical disease records, image and video data recording host morphology and behavior, aquaculture environment data detected by water quality detection sensors, the number and type of pathogenic microorganisms obtained via disease detection methods, as well as physiological and biochemical data related to diseases. The great volume of data generated provides researchers with the capacity to achieve aquatic disease detection and warning based on big data. Via the integration of big data with artificial intelligence and machine learning, this will enable an auxiliary decision-making platform to be established, reducing costs and labor and mitigating the threat of aquaculture disease.

This Special Issue aims to provide insights into using big data platforms and models to achieve the detection and warning of aquatic disease, and will lay the foundation for the successful control of aquaculture diseases in future industrial aquaculture processes.

We welcome the submission of full research articles, short communications, or review articles providing directions for future research or suggestions regarding how to better manage populations in a changed, future climate.

Prof. Dr. Fei Yin
Dr. Azmi Al-Jubury
Dr. Xiao Xie
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

aquatic diseases
big data
artificial intelligence
machine learning
auxiliary treatment decision-making tool

Published Papers (1 paper)

Research

14 pages, 4058 KiB

Open AccessArticle

Triple Attention Mechanism with YOLOv5s for Fish Detection

by Wei Long, Yawen Wang, Lingxi Hu, Jintao Zhang, Chen Zhang, Linhua Jiang and Lihong Xu

Fishes 2024, 9(5), 151; https://doi.org/10.3390/fishes9050151 - 23 Apr 2024

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Abstract

Traditional fish farming methods suffer from backward production, low efficiency, low yield, and environmental pollution. As a result of thorough research using deep learning technology, the industrial aquaculture model has experienced gradual maturation. A variety of complex factors makes it difficult to extract effective features, which results in less-than-good model performance. This paper proposes a fish detection method that combines a triple attention mechanism with a You Only Look Once (TAM-YOLO)model. In order to enhance the speed of model training, the process of data encapsulation incorporates positive sample matching. An exponential moving average (EMA) is incorporated into the training process to make the model more robust, and coordinate attention (CA) and a convolutional block attention module are integrated into the YOLOv5s backbone to enhance the feature extraction of channels and spatial locations. The extracted feature maps are input to the PANet path aggregation network, and the underlying information is stacked with the feature maps. The method improves the detection accuracy of underwater blurred and distorted fish images. Experimental results show that the proposed TAM-YOLO model outperforms YOLOv3, YOLOv4, YOLOv5s, YOLOv5m, and SSD, with a mAP value of 95.88%, thus providing a new strategy for fish detection. Full article

(This article belongs to the Special Issue Construction and Application of Big Data Platform and Model for the Detection and Warning of Aquatic Diseases)

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