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Fishes
Special Issues
Current Research on Fish Tracking Technology
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Current Research on Fish Tracking Technology

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: closed (26 January 2024) | Viewed by 5859

Special Issue Editors

Dr. Hideaki Tanoue

E-Mail Website
Guest Editor
General Planning and Coordination Department, Headquarters, Japan Fisheries Research and Education Agency, Yokohama 221-8529, Japan
Interests: operations evaluation; evidence-based decision making; biodiversity; ecosystem-based approach; marine spatial planning; integrated coastal management; habitat use; fisheries stock assessment and management; remote sensing; biologging
Dr. Terushisa Komatsu

E-Mail Website
Guest Editor
Japan Fisheries Resource Conservation Association, Towa-Akashi Building 5F, 1-1 Akashi, Chuo, Tokyo 104-0044, Japan
Interests: remote sensing; acoustics; satellite image; seaweed; seagrass; echosounder; multibeam sonar; data logger; biotelemetry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding the ecology of target species is essential for the sustainable use of fishery resources and the conservation of endangered species. This requires the measurement of the movements of individual animals in the water at different time scales. Based on this need, data loggers and acoustic tags have been developed to measure the movements of individuals in two to three dimensions, becoming smaller and smaller over the years and enabling long-term measurements. In fish, it has become possible to analyse stable isotopes recorded on otoliths and, as a result, to determine changes in habitats based on environmental history. More recently, the analysis of environmental DNA in water has also made it possible to study habitat use by providing data on the presence or absence of fish and, with some accuracy, estimates of abundance. In addition, fish population measurements using classical methods, such as fish finder and multibeam sonar, are beginning to reveal population-level distributions and movements. This Special Issue aims to summarise the state-of-the-art research results on the migration and distribution of fish using these methods. This Special Issue also welcomes research on behavioural analysis based on fish preferences using environmental parameters and computer simulations.

Dr. Hideaki Tanoue
Dr. Terushisa Komatsu
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

  • fish migration at individual and school level
  • fish behavior and distribution
  • acoustics
  • data logger
  • biological trace
  • tagging
  • biotelemetry
  • eDNA
  • individual-based models
  • remote sensing

Published Papers (3 papers)

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Research

18 pages, 4720 KiB  
Article
Transfer Learning Model Application for Rastrelliger brachysoma and R. kanagurta Image Classification Using Smartphone-Captured Images
by Roongparit Jongjaraunsuk, Wara Taparhudee, Soranuth Sirisuay, Methee Kaewnern, Varunthat Dulyapurk and Sommai Janekitkarn
Fishes 2024, 9(3), 103; https://doi.org/10.3390/fishes9030103 - 07 Mar 2024
Viewed by 1052
Abstract
Prior aquatic animal image classification research focused on distinguishing external features in controlled settings, utilizing either digital cameras or webcams. Identifying visually similar species, like Short mackerel (Rastrelliger brachysoma) and Indian mackerel (Rastrelliger kanagurta), is challenging without specialized knowledge. [...] Read more.
Prior aquatic animal image classification research focused on distinguishing external features in controlled settings, utilizing either digital cameras or webcams. Identifying visually similar species, like Short mackerel (Rastrelliger brachysoma) and Indian mackerel (Rastrelliger kanagurta), is challenging without specialized knowledge. However, advancements in computer technology have paved the way for leveraging machine learning and deep learning systems to address such challenges. In this study, transfer learning techniques were employed, utilizing established pre-trained models such as ResNet50, Xception, InceptionV3, VGG19, VGG16, and MobileNetV3Small. These models were applied to differentiate between the two species using raw images captured by a smartphone under uncontrolled conditions. The core architecture of the pre-trained models remained unchanged, except for the removal of the final fully connected layer. Instead, a global average pooling layer and two dense layers were appended at the end, comprising 1024 units and by a single unit, respectively. To mitigate overfitting concerns, early stopping was implemented. The results revealed that, among the models assessed, the Xception model exhibited the most promising predictive performance. It achieved the highest average accuracy levels of 0.849 and 0.754 during training and validation, surpassing the other models. Furthermore, fine-tuning the Xception model by extending the number of epochs yielded more impressive outcomes. After 30 epochs of fine-tuning, the Xception model demonstrated optimal performance, reaching an accuracy of 0.843 and displaying a 11.508% improvement in predictions compared to the model without fine-tuning. These findings highlight the efficacy of transfer learning, particularly with the Xception model, in accurately distinguishing visually similar aquatic species using smartphone-captured images, even in uncontrolled conditions. Full article
(This article belongs to the Special Issue Current Research on Fish Tracking Technology)
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17 pages, 5317 KiB  
Article
Individual Fish Echo Detection Method Based on Peak Delay Estimation and Instantaneous Frequency Characterization
by Hang Yang, Jing Cheng, Guodong Li, Taolin Tang and Jun Chen
Fishes 2023, 8(12), 580; https://doi.org/10.3390/fishes8120580 - 28 Nov 2023
Viewed by 1200
Abstract
In fisheries science research and farmed fish monitoring, acquiring individual fish echoes is the basis for the growth assessment, tracking, and target strength estimation of farmed fish. However, individual fish echo detection methods based on narrowband signal features cannot be applied well to [...] Read more.
In fisheries science research and farmed fish monitoring, acquiring individual fish echoes is the basis for the growth assessment, tracking, and target strength estimation of farmed fish. However, individual fish echo detection methods based on narrowband signal features cannot be applied well to high-density aquaculture scenarios. The broadband signaling system can improve the distance resolution of the detected target and can help to improve the performance of individual fish echo detection. In this study, for the broadband signal system and the characteristics of the underwater fish acoustic echoes, an individual fish echo detection method is proposed using the matched filter output envelope peak interval and instantaneous frequency characteristics of the echo as evaluation indices, and the simulation and experiments of the method are carried out in an anechoic water tank. The results show that the broadband signal system and the corresponding detection method perform better in detecting single target echoes than the narrowband signal system. Compared with the broadband single echo detection method that only relies on the peak interval of the matched filter envelope, the joint detection method that incorporates the instantaneous frequency characteristics of the echo signal has a better rejection capability for overlapping echoes, which can reduce the probability of misjudging the overlapping echoes. The combined detection methods may provide a better detection performance for individual fish echoes. Full article
(This article belongs to the Special Issue Current Research on Fish Tracking Technology)
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14 pages, 4557 KiB  
Article
Diseased Fish Detection in the Underwater Environment Using an Improved YOLOV5 Network for Intensive Aquaculture
by Zhen Wang, Haolu Liu, Guangyue Zhang, Xiao Yang, Lingmei Wen and Wei Zhao
Fishes 2023, 8(3), 169; https://doi.org/10.3390/fishes8030169 - 21 Mar 2023
Cited by 4 | Viewed by 3046
Abstract
In intensive aquaculture, the real-time detection and monitoring of common infectious disease is an important basis for scientific fish epidemic prevention strategies that can effectively reduce fish mortality and economic loss. However, low-quality underwater images and low-identification targets present great challenges to diseased [...] Read more.
In intensive aquaculture, the real-time detection and monitoring of common infectious disease is an important basis for scientific fish epidemic prevention strategies that can effectively reduce fish mortality and economic loss. However, low-quality underwater images and low-identification targets present great challenges to diseased fish detection. To overcome these challenges, this paper proposes a diseased fish detection model, using an improved YOLOV5 network for aquaculture (DFYOLO). The specific implementation methods are as follows: (1) the C3 structure is used instead of the CSPNet structure of the YOLOV5 model to facilitate the industrial deployment of the algorithm; (2) all the 3 × 3 convolutional kernels in the backbone network are replaced by a convolutional kernel group consisting of parallel 3 × 3, 1 × 3 and 3 × 1 convolutional kernels; and (3) the convolutional block attention module is added to the YOLOV5 algorithm. Experimental results in a fishing ground showed that the DFYOLO is better than that of the original YOLOV5 network, and the average precision was improved from 94.52% to 99.38% (when the intersection over union is 0.5), for an increase of 4.86%. Therefore, the DFYOLO network can effectively detect diseased fish and is applicable in intensive aquaculture. Full article
(This article belongs to the Special Issue Current Research on Fish Tracking Technology)
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