In recent years, there have been frequent jellyfish outbreaks in Chinese coastal waters, significantly impacting the structure, functionality, safety, and economy of nuclear power plant cooling water intake and nearby ecosystems. Therefore, this study focuses on jellyfish outbreaks in Chinese coastal waters, particularly near the Shandong Peninsula. By analyzing jellyfish abundance data, a Generalized Additive Model integrating environmental factors reveals that temperature and salinity greatly influence jellyfish density. The results show variations in jellyfish density among years, with higher densities in coastal areas. The model explains 42.2% of the variance, highlighting the positive correlation between temperature (20–26 °C) and jellyfish density, as well as the impact of salinity (27.5–29‰). Additionally, ocean currents play a significant role in nearshore jellyfish aggregation, with a correlation between ocean currents and site coordinates. This study aims to investigate the relationship between jellyfish blooms and environmental factors. The results obtained from the study provide data support for the prevention and control of blockages in nuclear power plant cooling systems, and provide a data basis for the implementation of monitoring measures in nuclear power plants.

Artificial reefs are man-made structures submerged in the ocean, and the design of these structures plays a crucial role in determining their effectiveness. Precisely measuring the configuration of artificial reefs is vital for creating suitable habitats for marine organisms. This study presents a novel approach for automated detection of artificial reefs by recognizing their key features and key points. Two enhanced models, namely, YOLOv8n-PoseRFSA and YOLOv8n-PoseMSA, are introduced based on the YOLOv8n-Pose architecture. The YOLOv8n-PoseRFSA model exhibits a 2.3% increase in accuracy in pinpointing target key points compared to the baseline YOLOv8n-Pose model, showcasing notable enhancements in recall rate, mean average precision (mAP), and other evaluation metrics. In response to the demand for swift identification in mobile fishing scenarios, a YOLOv8n-PoseMSA model is proposed, leveraging MobileNetV3 to replace the backbone network structure. This model reduces the computational burden to 33% of the original model while preserving recognition accuracy and minimizing the accuracy drop. The methodology outlined in this research enables real-time monitoring of artificial reef deployments, allowing for the precise quantification of their structural characteristics, thereby significantly enhancing monitoring efficiency and convenience. By better assessing the layout of artificial reefs and their ecological impact, this approach offers valuable data support for the future planning and implementation of reef projects.

Fish body measurement is essential for monitoring fish farming and evaluating growth. Non-destructive underwater measurements play a significant role in aquaculture management. This study involved annotating images of fish in aquaculture settings and utilized a line laser for underwater distance calibration and fish body inclined-angle calculation. The YOLOv8 model was employed for fish identification and key-point detection, enabling the determination of actual body dimensions through a mathematical model. The results show a root-mean-square error of 6.8 pixels for underwater distance calibration using the line laser. The pre-training YOLOv8-n, with its lower parameter counts and higher MAP values, proved more effective for fish identification and key-point detection, considering speed and accuracy. Average body length measurements within 1.5 m of the camera showed a minor deviation of 2.46% compared to manual measurements. The average relative errors for body length and width were 2.46% and 5.11%, respectively, with corresponding average absolute errors. This study introduces innovative techniques for fish body measurement in aquaculture, promoting the digitization and informatization of aquaculture processes.

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.

The large yellow croaker (Larimichthys crocea) is one of China’s most economically important marine fish species, with its cage culture production leading the nation for many years. However, the rapid expansion of aquaculture has brought challenges such as germplasm degradation, reduced disease resistance, inconsistent product quality, and low adoption of improved strains, which have hindered the sustainable development of the industry. The primary objective of this review is to summarize the current practices and challenges in seedling selection for L. crocea. The secondary objectives include discussing the influence of genetic, physiological, and environmental factors on growth performance and proposing future research directions for sustainable breeding programs. This review covers key topics including morphological screening, growth performance evaluation, genetic diversity conservation, disease resistance improvement, and adaptation to environmental stress. It also explores the application of modern technologies such as marker-assisted selection, intelligent monitoring, environmental control, precision feeding, and disease prevention. Moreover, it highlights core issues in current breeding practices, such as over-reliance on single-trait selection and insufficient integration of environmental adaptability and disease resistance. Finally, future trends are discussed, emphasizing the integration of genomic tools with artificial intelligence to promote intelligent, precise, and sustainable breeding approaches. These insights aim to enhance aquaculture productivity while supporting long-term ecological balance and industry sustainability.