Tuna accounts for 20% of the output value of global marine capture fisheries, and it plays a crucial role in maintaining ecosystem stability, ensuring global food security, and supporting economic stability. However, improper management has led to significant overfishing, resulting in a sharp decline in tuna populations. For sustainable tuna fishing, it is essential to accurately identify the species of tuna caught and to count their numbers, as these data are the foundation for setting scientific catch quotas. The traditional manual identification method suffers from several limitations and is prone to errors during prolonged operations, especially due to factors like fatigue, high-intensity workloads, or adverse weather conditions, which ultimately compromise its accuracy. Furthermore, the lack of transparency in the manual process may lead to intentional underreporting, which undermines the integrity of fisheries’ data. In contrast, an intelligent, real-time identification system can reduce the need for human labor, assist in more accurate identification, and enhance transparency in fisheries’ management. This system not only provides reliable data for refined management but also enables fisheries’ authorities to dynamically adjust fishing strategies in real time, issue timely warnings when catch limits are approached or exceeded, and prevent overfishing, thus ultimately contributing to sustainable tuna management. In light of this need, this article proposes the RSNC-YOLO algorithm, an intelligent model designed for recognizing tuna in complex scenarios on fishing vessels. Based on YOLOv8s-seg, RSNC-YOLO integrates Reparameterized C3 (RepC3), Selective Channel Down-sampling (SCDown), a Normalization-based Attention Module (NAM), and C2f-DCNv3-DLKA modules. By utilizing a subset of images selected from the Fishnet Open Image Database, the model achieves a 2.7% improvement in mAP@0.5 and a 0.7% improvement in mAP@0.5:0.95. Additionally, the number of parameters is reduced by approximately 30%, and the model’s weight size is reduced by 9.6 MB, while maintaining an inference speed comparable to that of YOLOv8s-seg.

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.

Measuring the distribution of the fishing effort of trawlers is of great significance for describing marine fishery activities, quantifying fishing systems in terms of marine ecological pressure, and revising the regulations of fishing. The purpose of this paper is to develop an efficient learning algorithm to detect the fishing behavior of trawlers to analyze the distribution of fishing effort. The vessel-monitoring system data of more than 4600 trawlers from September 2019 to April 2023 were used for feature extraction. According to the spatiotemporal information provided by the vessel position data, 11-dimensional features were extracted to form the feature vectors. A Slime Mould Algorithm-optimized Light Gradient-Boosting Machine (SMA-LightGBM) algorithm was proposed to classify the feature vectors to recognize fishing behavior. The presented method showed a remarkable generalization ability and high accuracy, sensitivity, specificity, and Matthews correlation coefficient in the test results, with scores of 98.23%, 98.75%, 97.75%, and 0.9646, respectively. Subsequently, the trained model was used to identify the fishing behavior of trawlers belonging to the coastal provinces of the Bohai Sea and the Yellow Sea in the sea area of ${117}^{°} \mathrm{E}~{132}^{°} \mathrm{E}$, ${26}^{°} \mathrm{N}~{41}^{°} \mathrm{N}$. The fishing effort was calculated and evaluated according to the fishing behavior recognition results. The mean absolute error was 0.3031 kW·h, and the coefficient of determination score was 0.9772. The thermal map of the fishing effort of the trawler was mapped, and the spatiotemporal characteristics were estimated in the region of interest from 2019 to 2023 with a spatial resolution of $\frac{1}{8}$ degree × $\frac{1}{8}$ degree. This method is an efficient way of analyzing the spatiotemporal characteristics of the fishing effort of trawlers. It provides a quantitative basis for the assessment of fishery resources and can inform fishing policies.

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.

Accurate and automated catch counting is crucial for modern fishery management, particularly in pelagic longline tuna fisheries where traditional manual methods are inefficient and error-prone. While computer vision offers solutions, its performance in complex, occlusion-heavy deck environments remain challenging due to severe occlusion and motion blur. This study proposes an uncertainty label-driven method for automated tuna counting to address these issues. Our approach introduces a closed polygonal counting region for improved coverage, an “uncertainty” label to handle classification ambiguity, and an enhanced ByteTrack algorithm for consistent target tracking. A lightweight YOLOv8-based model, optimized with efficient modules, ensures real-time operation. Experimental results demonstrate that the improved model achieves a mAP@0.5 of 0.958 and mAP@0.5:0.95 of 0.829, outperforming the baseline while reducing parameters and latency by 18.9% and 25.0%, respectively. In practical deck scenarios, the system reached 92.78% counting accuracy, a 14.43 percentage-point improvement. These findings validate the method’s enhanced robustness and stability under challenging conditions, offering a viable solution for automated catch monitoring and supporting further fishery informatization.