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Fishes
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AI and Fisheries
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AI and Fisheries

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 May 2024 | Viewed by 11548

Special Issue Editors

Prof. Dr. Xinjun Chen

E-Mail Website
Guest Editor
College of Marine Sciences, Shanghai Ocean University, Shanghai, China
Interests: fisheries biology; fishery oceanography; climate change and fish/fisheries; stock assessment; oceanic squid; fisheries forecasting; habitats; fisheries bio-economics and management
Dr. Bin Liu

E-Mail Website
Guest Editor
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
Interests: artificial intelligence oceanography; radar image processing and understanding; image segmentation and classification; artificial intelligence fishery

Special Issue Information

Dear Colleagues,

Since the last decade, artificial intelligence (AI) technology, especially deep learning, has been increasingly applied to computer vision, medical image processing, earth sciences, and other fields due to its powerful non-linear representation, feature learning, end-to-end modelling, and information mining capabilities. AI technology has not only achieved significant performance improvements in various fields, but also led to a paradigm shift in scientific discovery. Therefore, we expect AI to become essential for solving complex problems in fishery sciences. To this end, this Special Issue intends to collect the results of academic applications of deep learning and other AI technologies to fisheries, including AI dataset construction for fisheries, AI technology systems, and data standards based on fishery characteristics, species and population identification, biomass estimation, habitat assessment, fishery forecasting, and other fields.

Prof. Dr. Xinjun Chen
Dr. Bin Liu
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

  • AI
  • deep learning
  • fish identification
  • habitat
  • fisheries forecasting
  • biomass estimation

Published Papers (7 papers)

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Research

14 pages, 1941 KiB  
Article
Predicting the Fishery Ground of Jumbo Flying Squid (Dosidicus gigas) off Peru by Extracting Features of the Ocean Environment
by Tianjiao Zhang, Jia Xin, Wei Yu, Hongchun Yuan, Liming Song and Zhuo Yang
Fishes 2024, 9(3), 81; https://doi.org/10.3390/fishes9030081 - 21 Feb 2024
Viewed by 1029
Abstract
We introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terra satellite data [...] Read more.
We introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terra satellite data on sea surface temperature (SST) to construct a deep convolutional embedded clustering (DCEC) model and extract the monthly SST features (FM) based on an optimized number of clusters determined by the Davies–Bouldi index (DBI). We use the extracted FM to construct a series of Generalized Additive Models (GAM) to forecast the catch per unit effort (CPUE) of jumbo flying squid within a spatial resolution of 0.5° × 0.5°. Our results demonstrate the following findings: (1) The SST feature clusters obtained through the DCEC model could capture the SST monthly variations; (2) The GAM models with FM outperform the models with the traditional monthly average SST in terms of predictive accuracy; (3) Using both FM and average SST together can further improve model performance. This study demonstrates the effectiveness of the DCEC combined with DBI in extracting marine environmental features and highlights the ocean environment feature extraction method to enhance the precision and reliability of fishery forecasting models. Full article
(This article belongs to the Special Issue AI and Fisheries)
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17 pages, 4699 KiB  
Article
Deep Learning-Based Fishing Ground Prediction Using Asymmetric Spatiotemporal Scales: A Case Study of Ommastrephes bartramii
by Mingyang Xie, Bin Liu, Xinjun Chen, Wei Yu and Jintao Wang
Fishes 2024, 9(2), 64; https://doi.org/10.3390/fishes9020064 - 04 Feb 2024
Cited by 1 | Viewed by 1296
Abstract
Selecting the optimal spatiotemporal scale in fishing ground prediction models can maximize prediction accuracy. Current research on spatiotemporal scales shows that they are symmetrically distributed, which may not capture specific oceanographic features conducive to fishing ground formation. Recent studies have shown that deep [...] Read more.
Selecting the optimal spatiotemporal scale in fishing ground prediction models can maximize prediction accuracy. Current research on spatiotemporal scales shows that they are symmetrically distributed, which may not capture specific oceanographic features conducive to fishing ground formation. Recent studies have shown that deep learning is a promising research direction for addressing spatiotemporal scale issues. In the era of big data, deep learning outperforms traditional methods by more accurately and efficiently mining high-value, nonlinear information. In this study, taking Ommastrephes bartramii in the Northwest Pacific as an example, we used the U-Net model with sea surface temperature (SST) as the input factor and center fishing ground as the output factor. We constructed 80 different combinations of temporal scales and asymmetric spatial scales using data in 1998–2020. By comparing the results, we found that the optimal temporal scale for the deep learning fishing ground prediction model is 15 days, and the spatial scale is 0.25° × 0.25°. Larger time scales lead to higher model accuracy, and latitude has a greater impact on the model than longitude. It further enriches and refines the criteria for selecting spatiotemporal scales. This result deepens our understanding of the oceanographic characteristics of the Northwest Pacific environmental field and lays the foundation for future artificial intelligence-based fishery research. This study provides a scientific basis for the sustainable development of efficient fishery production. Full article
(This article belongs to the Special Issue AI and Fisheries)
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14 pages, 9118 KiB  
Article
Advanced Robotic System with Keypoint Extraction and YOLOv5 Object Detection Algorithm for Precise Livestock Monitoring
by Balaji Natesan, Chuan-Ming Liu, Van-Dai Ta and Raymond Liao
Fishes 2023, 8(10), 524; https://doi.org/10.3390/fishes8100524 - 21 Oct 2023
Viewed by 1306
Abstract
Molting is an essential operation in the life of every lobster, and observing this process will help us to assist lobsters in their recovery. However, traditional observation consumes a significant amount of time and labor. This study aims to develop an autonomous AI-based [...] Read more.
Molting is an essential operation in the life of every lobster, and observing this process will help us to assist lobsters in their recovery. However, traditional observation consumes a significant amount of time and labor. This study aims to develop an autonomous AI-based robot monitoring system to detect molt. In this study, we used an optimized Yolov5s algorithm and DeepLabCut tool to analyze and detect all six molting phases such as S1 (normal), S2 (stress), S3–S5 (molt), and S6 (exoskeleton). We constructed the proposed optimized Yolov5s algorithm to analyze the frequency of posture change between S1 (normal) and S2 (stress). During this stage, if the lobster stays stressed for 80% of the past 6 h, the system will assign the keypoint from the DeepLabCut tool to the lobster hip. The process primarily concentrates on the S3–S5 stage to identify the variation in the hatching spot. At the end of this process, the system will re-import the optimized Yolov5s to detect the presence of an independent shell, S6, inside the tank. The optimized Yolov5s embedded a Convolutional Block Attention Module into the backbone network to improve the feature extraction capability of the model, which has been evaluated by evaluation metrics, comparison studies, and IoU comparisons between Yolo’s to understand the network’s performance. Additionally, we conducted experiments to measure the accuracy of the DeepLabCut Tool’s detections. Full article
(This article belongs to the Special Issue AI and Fisheries)
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17 pages, 38616 KiB  
Article
An Improved Deep Learning Model for Underwater Species Recognition in Aquaculture
by Mahdi Hamzaoui, Mohamed Ould-Elhassen Aoueileyine, Lamia Romdhani and Ridha Bouallegue
Fishes 2023, 8(10), 514; https://doi.org/10.3390/fishes8100514 - 16 Oct 2023
Viewed by 2814
Abstract
The ability to differentiate between various fish species plays an essential role in aquaculture. It helps to protect their populations and monitor their health situations and their nutrient systems. However, old machine learning methods are unable to detect objects in images with complex [...] Read more.
The ability to differentiate between various fish species plays an essential role in aquaculture. It helps to protect their populations and monitor their health situations and their nutrient systems. However, old machine learning methods are unable to detect objects in images with complex backgrounds and especially in low-light conditions. This paper aims to improve the performance of a YOLO v5 model for fish recognition and classification. In the context of transfer learning, our improved model FishDETECT uses the pre-trained FishMask model. Then it is tested in various complex scenes. The experimental results show that FishDETECT is more effective than a simple YOLO v5 model. Using the evaluation metrics Precision, Recall, and mAP50, our new model achieved accuracy rates of 0.962, 0.978, and 0.995, respectively. Full article
(This article belongs to the Special Issue AI and Fisheries)
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20 pages, 16539 KiB  
Article
Behavior Recognition of Squid Jigger Based on Deep Learning
by Yifan Song, Shengmao Zhang, Fenghua Tang, Yongchuang Shi, Yumei Wu, Jianwen He, Yunyun Chen and Lin Li
Fishes 2023, 8(10), 502; https://doi.org/10.3390/fishes8100502 - 08 Oct 2023
Cited by 1 | Viewed by 1177
Abstract
In recent years, with the development of pelagic fishing, the working environment and monitoring of crew (squid jigger) members have become increasingly important. However, traditional methods of pelagic human observers suffer from high costs, low coverage, poor timeliness, and susceptibility to subjective factors. [...] Read more.
In recent years, with the development of pelagic fishing, the working environment and monitoring of crew (squid jigger) members have become increasingly important. However, traditional methods of pelagic human observers suffer from high costs, low coverage, poor timeliness, and susceptibility to subjective factors. In contrast, the Electronic Monitoring System (EMS) has advantages such as continuous operation under various weather conditions; more objective, transparent, and efficient data; and less interference with fishing operations. This paper shows how the 3DCNN model, LSTM+ResNet model, and TimeSformer model are applied to video-classification tasks, and for the first time, they are applied to an EMS. In addition, this paper tests and compares the application effects of the three models on video classification, and discusses the advantages and challenges of using them for video recognition. Through experiments, we obtained the accuracy and relevant indicators of video recognition using different models. The research results show that when NUM_FRAMES is set to 8, the LSTM+ResNet-50 model has the best performance, with an accuracy of 88.47%, an F1 score of 0.8881, and an map score of 0.8133. Analyzing the EMS for pelagic fishing can improve China’s performance level and management efficiency in pelagic fishing, and promote the development of the fishery knowledge service system and smart fishery engineering. Full article
(This article belongs to the Special Issue AI and Fisheries)
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Graphical abstract

16 pages, 40655 KiB  
Article
A Real-Time Lightweight Detection Algorithm for Deck Crew and the Use of Fishing Nets Based on Improved YOLOv5s Network
by Jiaming Wang, Xiangbo Yin and Guodong Li
Fishes 2023, 8(7), 376; https://doi.org/10.3390/fishes8070376 - 20 Jul 2023
Viewed by 881
Abstract
A real-time monitoring system for the operational status of fishing vessels is an essential element for the modernization of the fishing industry. The operational status of fishing vessels can be identified by using onboard cameras to detect the deck crew and the use [...] Read more.
A real-time monitoring system for the operational status of fishing vessels is an essential element for the modernization of the fishing industry. The operational status of fishing vessels can be identified by using onboard cameras to detect the deck crew and the use of fishing nets. Due to the typically limited processing capacity of shipboard equipment and the significant memory consumption of detection models, general target detection models are unable to perform real-time image detection to identify the operational status of fishing vessels. In this paper, we propose a lightweight real-time deck crew and the use of a fishing net detection method, YOLOv5s-SGC. It is based on the YOLOv5s model, which uses surveillance cameras to obtain video of fishing vessels operating at sea and enhances the dataset. YOLOv5s-SGC replaces the backbone of YOLOv5s with ShuffleNetV2, replaces the feature fusion network with a modified Generalized-FPN, and adds the CBAM attention module in front of the detection head. Full article
(This article belongs to the Special Issue AI and Fisheries)
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Graphical abstract

19 pages, 9426 KiB  
Article
Automated Identification of Morphological Characteristics of Three Thunnus Species Based on Different Machine Learning Algorithms
by Liguo Ou, Bilin Liu, Xinjun Chen, Qi He, Weiguo Qian and Leilei Zou
Fishes 2023, 8(4), 182; https://doi.org/10.3390/fishes8040182 - 29 Mar 2023
Cited by 2 | Viewed by 1893
Abstract
Tuna are economically important fish species. The automated identification of tuna species is of importance in fishery production and resource assessment in that it would facilitate the informed monitoring of tuna fishing vessels and the establishment of electronic observer systems. As morphological characteristics [...] Read more.
Tuna are economically important fish species. The automated identification of tuna species is of importance in fishery production and resource assessment in that it would facilitate the informed monitoring of tuna fishing vessels and the establishment of electronic observer systems. As morphological characteristics are important for tuna identification, this study aims to verify the performance of the automated identification of three Thunnus species through morphological characteristics based on different machine learning algorithms. Firstly, morphological outlines were visually analyzed using EFT (elliptic Fourier transform) and CNN (convolutional neural network). Then, the EFT feature data and deep feature data of the tuna outline images were extracted, and principal component analysis of the two different morphological characteristics was performed. Finally, different machine learning algorithms were used to analyze the identification performance of tuna of the same genus and different species. The experimental results showed that EFT features had the highest identification accuracy in KNN (K-nearest neighbor), with 90% for T. obesus, 90% for T. albacores, and 85% for T. alalunga. Deep features had the best identification performance in SVM (support vector machine), with 80% for T. obesus, 90% for T. albacores, and 100% for T. alalunga. Deep features were better than EFT features in identification performance. The biodiversity and intergeneric differences among tuna species can be well analyzed using these two different morphological characteristics. Machine learning algorithms open up the way for rapid near-real-time electronic observer systems in these important international fisheries. Full article
(This article belongs to the Special Issue AI and Fisheries)
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