Smart fisheries are an important way to promote the sustainable development of fisheries, and efficient and reliable marine communication systems are the key to realizing smart fisheries. As an emerging marine communication technology, the VHF Data Exchange System (VDES) has the advantages of a high data transmission rate, large communication capacity, and wide coverage, providing new opportunities for the transformation and upgrading of smart fisheries. This paper introduces the technical architecture and functions of the VDES, compares it with existing marine communication technologies, analyzes the key requirements of a smart fishery, and assesses how the VDES meets these requirements. The potential application scenarios of the VDES in smart fishery fields such as fishing vessel monitoring, fishery resource management, and maritime security are discussed. The challenges faced by the VDES in the application of smart fisheries, such as technology, policies and regulations, and construction cost layout, are analyzed, and its future development trend is prospected. Suggestions such as its integration with emerging technologies, the realization of global seamless coverage, and the strengthening of international cooperation and data sharing are proposed. This paper aims to provide theoretical guidance and scientific reference for the promotion and application of the VDES in smart fisheries.

Siniperca chuatsi, commonly known as mandarin fish, is one of the most economically valuable freshwater species in China. In 2022, the national aquaculture production of mandarin fish reached approximately 401,000 tons, accounting for a significant share of freshwater aquaculture in China and nearly dominating the global commercial farming landscape. With the rapid development of recirculating aquaculture systems (RASs), higher requirements have been raised for feeding efficiency and fish health monitoring. Traditional on-site visual observation methods are highly subjective, inefficient, difficult to quantify, and prone to misjudgment under conditions such as insufficient illumination, turbid water, or high stocking density. To address these challenges, this study proposes FishSegNet-PRL, an instance segmentation-based model designed to quantify the feeding intensity of mandarin fish. The model is built upon the YOLOv11-seg framework, enhanced with a P2 detection layer (P), a residual cross-stage spatial–channel attention module (RCSOSA, R), and a lightweight semantic-detail-enhanced cascaded decoder (LSDECD, L). These improvements collectively enhance small-target detection capability, boundary segmentation accuracy, and real-time inference performance. Experimental results demonstrate that FishSegNet-PRL achieves superior performance in mandarin fish instance segmentation, with a Box mAP50 of 85.7% and a Mask mAP50 of 79.4%, representing improvements of approximately 4.6% and 13.2%, respectively, compared with the baseline YOLOv11-seg model. At the application level, multiple feeding intensity quantification indices were constructed based on the segmentation results and evaluated, achieving a temporal intersection-over-union (IoU_time) of 95.9%. Overall, this approach enables objective and fine-grained assessment of mandarin fish feeding behavior, striking an effective balance between accuracy and real-time performance. It provides a feasible and efficient technical solution for intelligent feeding and behavioral monitoring in aquaculture.

Offshore and deep-sea aquaculture is increasingly recognized as a key pathway for expanding marine food production as nearshore resources decline and global demand for high-quality aquatic products grows. However, open-ocean farming operates under highly dynamic environmental conditions and long production cycles, which impose significant challenges on conventional experience-based management. This review synthesizes recent research on informatized management in offshore and deep-sea aquaculture and proposes a structured management framework based on five functional layers: perception, transmission, platform, decision, and execution. By systematically analyzing environmental constraints, technical bottlenecks, and management requirements, this framework integrates key technologies including the Internet of Things, unmanned surface and underwater vehicles, big data analytics, and artificial intelligence. The review further examines representative application scenarios, including environmental monitoring and early warning, intelligent feeding and nutrition management, disease prevention and control, and remote monitoring and management. Through cross-study comparison, this work highlights current limitations in system integration and long-term validation, while clarifying the technological pathways required for scalable and reliable offshore deployment. Overall, this review provides a conceptual foundation and technical reference for improving operational safety, production efficiency, and environmental sustainability in offshore and deep-sea aquaculture.

Traditional morphometric analysis of Large Yellow Croaker (Larimichthys crocea) relies heavily on manual dissection, which is time-consuming, labor-intensive, and prone to subjectivity. To address these limitations, we propose an automated quantitative approach based on deep-learning–driven instance segmentation. A dataset comprising 160 X-ray images of L. crocea was established, encompassing five anatomical categories: whole fish, air bladder, spine, eyes, and otoliths. Building upon the baseline YOLOv11-Seg model, we integrated a lightweight Convolutional Block Attention Module (CBAM) to construct an improved YOLOv11-CBAM network, thereby enhancing segmentation accuracy for complex backgrounds and fine-grained targets. Experimental results demonstrated that the modified model achieved superior performance in both mAP50 and mAP50–95 compared with the baseline, with particularly notable improvements in the segmentation of small-scale structures such as the air bladder and spine. By introducing coin-based calibration, pixel counts were converted into absolute areas and relative proportions. The measured area ratios of the air bladder, otoliths, eyes, and spine were 7.72%, 0.59%, 2.20%, and 8.48%, respectively, with standard deviations remaining within acceptable ranges, thus validating the robustness of the proposed method. Collectively, this study establishes a standardized, efficient, and non-destructive workflow for X-ray image-based morphometric analysis, providing practical applications for aquaculture management, germplasm conservation, and fundamental biological research.

High-resolution spatiotemporal prediction information on fishing vessel activities is essential for formulating and effectively implementing fisheries policies that ensure the sustainability of marine resources and fishing practices. This study focused on the tuna longline fishery in the Western and Central Pacific Ocean (130° E–150° W, 20° S–20° N) and constructed a CLA U-Net deep learning model to predict fishing effort (FE) distribution based on 2017–2023 FE records and environmental variables. Two modeling schemes were designed: Scheme 1 incorporated both historical FE and environmental data, while Scheme 2 used only environmental variables. The model predicts not only the binary outcome (presence or absence of fishing effort) but also the magnitude of FE. Results show that in short-term predictions, Scheme 1 achieved F1 scores of 0.654 at the 0.5°-1-day scale and 0.763 at the 1°-1-day scale, indicating substantial improvement from including historical FE data. In medium-term predictions, Scheme 1 and Scheme 2 reached maximum F1 scores of 0.77 and 0.72, respectively, at the optimal spatiotemporal scale of 1°-30 days. The analysis also quantified the relative importance of environmental variables, with sea surface temperature (SST) and chlorophyll-a (Chl-a) identified as the most influential. These findings provide methodological insights for spatiotemporal prediction of fishing effort and support the refinement of fisheries management and sustainability strategies.

Morphological differences between sea-caught and farmed fish reflect environmental conditions and long-term domestication. However, standardized and objective quantification of these differences remains limited for many commercially important species. The large yellow croaker (Larimichthys crocea) represents a typical marine fish with clear contrasts between natural and aquaculture production systems. In this study, an image-based phenotyping workflow was developed to quantify external morphological traits of sea-caught and farmed L. crocea. Visible-light images were acquired under standardized conditions. A YOLOv11-based instance segmentation model was applied to automatically delineate major anatomical regions, including the body, head, eyes, pectoral fins, and tail. Surface areas and proportional indices were calculated following geometric calibration to ensure measurement consistency. The segmentation model achieved high accuracy on the test dataset (mAP@50 > 98%). Morphometric analyses revealed clear differences between the two groups. Farmed individuals exhibited larger body-related surface areas, whereas the relative proportions of pectoral fins and tail regions were reduced. Sea-caught fish showed higher proportional investment in locomotor structures, consistent with the physical demands of natural marine environments. These results indicate a shift in morphological allocation associated with aquaculture, characterized by enhanced trunk growth and reduced relative development of propulsion-related structures. The proposed workflow provides a rapid, non-invasive, and reproducible approach for fish morphometric analysis. It offers practical potential for phenotypic monitoring and stock assessment, while contributing quantitative evidence for domestication-driven morphological divergence in marine fishes.