To investigate the potential of edge computing technology in intelligent fisheries equipment, this study addresses limitations of traditional cloud computing regarding real-time responsiveness and efficiency by proposing an optimized solution through relocating computational resources closer to the network edge. The research systematically reviews the development history of edge computing technology and emphasizes the critical technologies in intelligent fisheries equipment, such as computational offloading and data storage and management. By analyzing typical fishery application scenarios, the role of edge computing in improving real-time data processing and system responsiveness is highlighted. Results indicate that edge computing significantly alleviates network bandwidth constraints and transmission latency issues by decentralizing computational resources, thereby enhancing the real-time performance of intelligent fisheries equipment. Nevertheless, challenges such as limited computing capabilities of edge devices and insufficient coordination among heterogeneous equipment continue to hinder broader adoption. With deeper integration of edge computing with artificial intelligence, big data, and the Internet of Things (IoT), edge computing promises further improvements in remote data transmission, IoT integration, intelligent decision-making, and sustainable development in intelligent fisheries. This advancement is expected to drive the fisheries industry toward greater intelligence, efficiency, and ecological sustainability.

A real-time structural safety assessment method based on digital twin technology is proposed to ensure the safe and stable operation of the environmental monitoring platform of the sea ranch during its service period. A three-level digital twin architecture is adopted to achieve rapid prediction and real-time visualization of the overall stress distribution state of the monitoring platform. The maximum error is less than 10%, which verifies the reliability of the simulation model; the structural stress field response database covering the monitoring platform under the common sea conditions during the service period is established by batch front simulation calculation of multiple working conditions; the structural stress field response database covering the monitoring platform under the common sea conditions during the service period is established by batch front simulation calculation of multiple working conditions;   the structural stress field response database covering the monitoring platform under the common sea conditions during the service period is established by batch front simulation calculation of multiple working conditions; under the simultaneous change of environmental parameters, the structural stress distribution of the monitoring platform can be predicted and visualized in real time.   In the case of simultaneous changes of environmental parameters, a fast prediction based on the structural response database is carried out by the improved inverse distance weight interpolation (IIDW) method, and the results show that the average absolute errors between the interpolated data and the simulation data for axial forces, moments, and spatial displacements at the monitoring points are 7.62%, 11.93%, and 5.77%, respectively. The average absolute errors between interpolation data and simulation data for all 2462 structural rods were 6.24%, 7.88% and 5.39%, respectively. The rapid structural safety assessment method of the ocean ranch environmental monitoring platform proposed in this study provides a feasible solution for the real-time monitoring of the overall stress and safety early warning during the platform's service period.

Accurately and efficiently monitoring the stress behavior of fish fry not only helps to regulate stressors during the breeding process to reduce yield losses, but also provides an effective means for evaluating the vitality of fish fry during the breeding stage. In view of the characteristics of fish fry, such as small size, high stocking density, and high - speed non - linear movement, this study proposes a method for monitoring the stress behavior of fish fry by improving YOLOv8n - pose and combining it with BoTSORT.The improved YOLOv8n - pose is used as a detector. The BMS module is combined with the C2f module to enable the model to fully learn features at different scales. The SPPCSPC module is used to replace the original feature fusion module of the model to optimize the detection accuracy in the case of fish fry occlusion. Finally, N - EMASlideLoss is used to replace the original loss function of the model, enhancing the model's stability and attention to small targets.In the tracker part, based on the targets detected by the detector, a method more suitable for monitoring the non - linear movement of fish fry under stress is achieved by combining the BoTSORT multi - target tracking algorithm.Finally, three features of fish fry, namely acceleration, tail - wagging angle, and aggregation degree, are extracted and weighted for fusion. Based on the fused feature values, it is determined whether the fish fry are under stress. The experimental results show that the mAP of the improved YOLOv8n - pose algorithm in target detection and key - point detection is 3.6% and 4.5% higher than that of the original model respectively. The MOTA of the BoTSORT algorithm is 77.628%, the MOTP is 80.307%, the IDF1 is 79.573%, and the IDSW is 51, which are superior to those of the DeepSORT, ByteTrack, and StrongSORT algorithms. The accuracy of the stress behavior monitoring of this study's algorithm based on feature values is 95.24%, providing new ideas and methods for stress behavior monitoring in fish fry breeding. 

This study proposes a target detection and tracking method based on the improved YOLOv11 model—YOLOv11n-DFM. It aims to evaluate losses during the fishing process by detecting the number of crab traps being lifted or lowered and to assess the normalcy of the trap mechanism by detecting the number of traps in key areas. The method integrates DyHead, FocalModulation, and CCFM modules into the YOLOv11n model to enhance multi-scale feature fusion, improve detection accuracy for traps of different scales, and reduce computational and memory costs. Additionally, the ByteTrack algorithm is employed to ensure precise tracking of the traps. Experimental results demonstrate that the YOLOv11n-DFM model improves detection accuracy by 1%, increases mAP@50-95 by 0.8%, while mAP@50 and recall remain unchanged. Compared to the YOLOv11n model, the detection performance is enhanced while maintaining the same detection efficiency. The study indicates that the YOLOv11n-DFM model excels in detecting and tracking the crabs' traps, consumes fewer computational resources, and is suitable for deployment in environments with limited computing power. It provides valuable references for fishery monitoring, resource management, and the future automation of crab trap deployment and collection.

 Accurate assessment of fish appetite intensity is essential for achieving precise feeding in aquaculture. To address the current issues of low accuracy and poor real-time performance in fish appetite intensity evaluation, this study proposes a fish appetite intensity assessment model based on an improved MobileNetV2 and transfer learning. First, we enhance the MobileNetV2 architecture by introducing the CBAM attention mechanism module after the skip connections in inverted residual blocks, thereby significantly improving the model's ability to capture key feature information. Next, we collect surface video data of fish feeding behavior in actual aquaculture environments. Through keyframe extraction and redundancy removal using hash-based differential threshold methods, we construct a real-world fish appetite intensity dataset. Validation experiments on this dataset show that after 200 iterations, the optimized model outperforms other models, with a memory footprint of only 9.3 MB, achieving 92.75% accuracy, 92.92% recall, 92.65% precision, and 92.70% F1-score. The proposed model offers high assessment accuracy with a compact memory footprint, providing significant technical support for intelligent and precise aquaculture management.

In order to improve the bait weighing accuracy of the discharging device under the swaying and bumping conditions at sea, a weighing error correction algorithm based on PSO-BP neural network is proposed. Based on the sensing technology, a closed experimental device with load cell and attitude sensor was established, and the weighing and attitude data in the tilt range of 0-20 degrees under different counterweights were measured in the offshore farm, and after determining the correction coefficients, the BP neural network algorithm was introduced to obtain the predicted values of the weighing. In the test samples with real masses of 8.06 kg and 12.4 kg, the maximum relative errors of the weighing data corrected by the BP neural network algorithm were reduced by 4.32% and 4.36%, respectively, compared with the direct measurement method; the maximum relative errors of the weighing data corrected by the PSO-BP neural network algorithm were reduced by 0.39% and 0.33%, respectively, compared with the BP neural network algorithm. The maximum relative errors of the PSO-BP neural network algorithm were reduced by 0.39% and 0.33%, respectively. The PSO-BP neural network algorithm has a higher accuracy in error correction for bait weighing in offshore net-pen aquaculture.

In the freshwater snail product classification and processing scenario, accurately and efficiently identifying the sexes and dead features of Cipangopaludina cahayensis is crucial for the quality classification and grading of freshwater snail products. Distinguishing between male and female individuals allows for targeted selection of high-quality parents for snail seed breeding. Timely removal of rotten dead snails is important for maintaining water quality in aquaculture and disease prevention. Currently, the methods for identifying the sexes and dead features of Cipangopaludina cahayensis mainly include: 1) distinguishing between males and females based on differences in antennae observed in their natural state; 2) distinguishing between male and female glands under strong light transmission and observing the internal shrinkage of snails after death to differentiate. However, these methods suffer from issues such as high workload, subjectivity, high time costs, low detection efficiency, and high false detection rates. In response to the demands of modernizing China's fisheries, achieving automation and intelligence in the classification of male and female individuals and dead features of Cipangopaludina cahayensis is of significant importance for improving the technological development of freshwater snail factory farming and aquatic product classification and processing. Therefore, how to achieve accurate identification and rapid detection of the sexes and dead features in the processing of snail products is a pressing issue that needs to be addressed in the automation of quality classification and grading operations for Cipangopaludina cahayensis. Cipangopaludina cahayensis has a relatively late development in automated aquaculture compared to other aquatic organisms, with limited targeted research on intelligence. Additionally, existing algorithmic literature focuses solely on the detection of the phenotypes of freshwater snail shells, neglecting considerations such as model lightweighting, unbalanced detection accuracy, and real-time detection speed. The detection effectiveness of the algorithms for distinguishing the sexes and dead features of Cipangopaludina cahayensis still remains inadequate. In summary, this study adopts the YOLOv8n model as the base model and proposes a Cipangopaludina cahayensis male-female and death feature detection algorithm based on AP2O-YOLOv8. This research aims to provide a theoretical foundation and reference for the automation and intelligence of processes such as quality classification and grading of Cipangopaludina cahayensis products. In terms of model design, this study introduces the P2 layer for small target detection, incorporates larger-scale feature maps containing more information about snail target positions and inter-class local features, and combines the ASF-YOLO structure and C2f-OREPA module to further enhance the algorithm's multi-scale feature fusion capability and real-time detection speed. This approach allows the model to have higher detection performance while being more lightweight and efficient. The improved algorithm in this article integrates three enhancement schemes. Compared to the original YOLOv8, its precision (P), recall (R), and mean average precision at IOU 0.5 have increased by 2.1%, 2.6%, and 5.6% respectively. The parameter size has decreased from 2.9MB to 2.1MB, a reduction of 27.6%. The frames per second (FPS) have increased from 180 to 226, a 25.6% improvement. The AP2O-YOLOv8 model proposed in this article for the detection of male and female Cipangopaludina cahayensiss, as well as their vital status, significantly enhances the detection accuracy of different features of Cipangopaludina cahayensiss compared to the original benchmark model. Simultaneously, it effectively reduces the complexity of the model, greatly increasing real-time detection speed. This study provides new ideas and methods for the classification and detection of male and female, live and dead Cipangopaludina cahayensiss, helping further advance the automation and intelligence upgrade of the quality classification and processing process of Cipangopaludina cahayensiss.

Sea treasure target detection is a key technology for the intelligent development of sea treasure resources. This paper proposes an improved algorithm YOLOv9-PAEG based on YOLOv9-S to address the problem of low accuracy in detecting sea treasures in complex underwater environments, difficult feature extraction, diverse target sizes, and a large number of small targets. Firstly, the SPPELAN module was improved by introducing the PfAAM attention mechanism and distributed shift convolution DSConv2D, and the PFAD_SPPELAN module was designed to enhance the detection accuracy and speed of the model. Secondly, by introducing a variable kernel convolution AKConv in the backbone network layer of the model, the model can more flexibly adapt to features of different sizes and shapes, thereby improving its feature extraction ability for multi-scale targets, especially small targets. Then, the ECA attention mechanism was integrated into the neck layer of the model, enhancing its ability to represent important features and improving detection accuracy. Finally, by using the GIoU loss function, the convergence of the model was accelerated and the positioning accuracy was optimized. Experiments have shown that the YOLOv9-PAEG model performs well on datasets DUO and UDD mAP@0.5 They reached 89.7% and 77.6% respectively, and FPS reached 71 and 69, respectively. Compared with the original model and other mainstream object detection models, they have improved detection accuracy and speed. This fully proves the effectiveness and progressiveness of the YOLOv9-PAEG model, which can provide a better detection effect for marine treasures.

Zebrafish is a commonly used model organism for heart disease research, and its larval cardiac is transparent and able to be directly observed under the microscope, and no mature and effective algorithm has yet been developed to automatically identify the zebrafish cardiac. In order to improve the accuracy and real-time efficiency of automatic segmentation of zebrafish ventricular images, and to solve the problem of weak boundaries of the ventricular region, insufficient feature extraction capability, and insufficient utilization of inter-frame correlation details, this thesis proposes EGAFB, a method for zebrafish ventricular images segmentation based on an edge-guided adaptive feature bank. The encoder is improved by embedding a rectangular self-calibration module, which extracts the global context information and enhances the ventricular feature extraction; meanwhile, the edge-guided attention mechanism is introduced to direct the model to focus on more boundary detail information, which strengthens the ventricular boundary identification ability; mean square error loss and classification confidence loss functions are introduced to optimise the local refinement segmentation mechanism, which improves the zebrafish ventricle recognition accuracy. The results show that the mean Intersection over Union (mIoU) of the EGAFB reaches 94.7%. Compared with the existing method Unet, the mIoU is improved by 4.6% and the inference time is reduced by 22.9%; compared with the original model, the mIoU is improved by 1.3% and the inference time is reduced by 6.4%. This thesis shows that the EGAFB method has high accuracy and real-time segmentation efficiency, which provides an effective solution for automatic segmentation of zebrafish ventricular images, as well as some more efficient technical support for the research of zebrafish cardiac disease models. 

This research designed a scallop larval culture environment monitoring and control system based on a three-layer Internet of Things architecture to increase the survival rate of scallop larval cultivation. In order to achieve remote intelligent monitoring of the scallop larval cultivation environment, it is comprised of water quality monitoring, video monitoring, intelligent control, and a remote service center. The system's primary control center is an STM32 microcontroller, which gathers data on water quality via the ModBus protocol to provide real-time monitoring of dissolved oxygen, water temperature and liquid level; The Yingshi Cloud platform is used in the video surveillance to track the scallop larval's cultivation state and the water level of the cultivation cone; The device uses fuzzy neural network PID control to intelligently regulate the dissolved oxygen and water's temperature levels; Web and Android applications have been created by the application layer. The complete system network is linked to the Alibaba Cloud platform and uses a WiFi wireless network module. Users can remotely view the data about the cultivation environment using web browsers and Android application terminals thanks to the integrated server administration program. Build an experimental system and test the communication stability, data accuracy, and web application individually. The communication success rate of the complete system reaches above 99%, with an average relative measurement error of ±0.074mg/L for dissolved oxygen and ±0.079℃ for water temperature. The system has been operating steadily and dependably, supporting the equipment used in the scallop seedling business and satisfying the requirements of raising scallop larval in circulating water.

In the photovoltaic river crab breeding pond environment, solar panel obstruction significantly reduces the accuracy of the unmanned operation ship's satellite positioning system. To address this, a laser - inertia - based unmanned operation ship positioning method is proposed, considering the pond's unique conditions. This method improves the Hector - Slam positioning process. First, LiDAR point cloud data undergoes preprocessing to filter disturbances and reduce data size, enhancing accuracy. Then, the map continuity method in Hector - Slam is enhanced by using nonlinear fitting to identify obstacle centers, followed by Gaussian blurring to ensure map continuity, creating a smoother reference map for matching. Next, the map - matching process in Hector - Slam is improved by replacing the Gaussian Newton method with gradient descent, yielding more precise results. Finally, a Kalman filter integrates radar and IMU poses, combining position and heading angle information for improved positioning accuracy. Experimental results show the laser inertial fusion positioning method reduces average positioning deviation by 46% compared to the Hector algorithm. Unlike satellite positioning, which fails to meet the accuracy requirements in photovoltaic ponds, our laser - based method ensures precise positioning. It also outperforms visual schemes in accuracy under disturbances and low - light conditions. Moreover, compared to high - cost 3D laser solutions that are impractical for agricultural production, our cost - effective laser method offers significant advantages. Thus, this laser inertial fusion positioning method can replace satellite positioning, effectively meeting practical production needs.

n response to the variability of fishing boat trajectories, this study aims to improve the accuracy of the prediction model by optimizing the characteristic parameters of fishing boats during the data preprocessing stage, in order to enhance the accuracy of predicting fishing boat berthing trajectories. Propose a fishing vessel berthing trajectory prediction model based on Beidou ship position data and combined with Long Short Term Memory (LSTM) network. Collect Beidou fishing vessel position data through a Vessel Monitoring Systems (VMS) onboard terminal, extract spatiotemporal position information and other feature parameters, preprocess the collected Beidou fishing vessel position data, select input feature parameters for the prediction model using correlation analysis, classify the feature parameters according to fishing vessel size and type, and train the model. Finally, compare the predicted trajectory with the actual berthing trajectory. Exploring the practicality of Beidou ship position data in ship trajectory prediction and the impact of fishing vessel types on berthing trajectory prediction. The final experimental results showed that the accuracy of the model prediction reached 92.3%, proving the superiority of Beidou ship position data in ship trajectory prediction research. At the same time, it proved the conclusion that the type of fishing captain is positively correlated with the longitude of trajectory prediction, providing a new method for port and fishery management.

 High-density polyethylene (HDPE) is a novel thermoplastic material for fishing vessel construction, whose welding quality is one of the primary factors ensuring the safety of HDPE fishing vessels. To address the challenges in HDPE fishing vessel welds defect detection, including high similarity between defects and background as well as weak small-target features, this study proposes an improved ACA-YOLOv8(Adown-CCFM-AC-mix-YOLOv8) object detection algorithm.The proposed method employs an Adaptive Downsampling(ADown) strategy to effectively preserve defect features, enhances multi-scale feature representation through a Cross-scale Consistent Feature Fusion Network(CCFM), and incorporates a Self-attention and Convolution Mixed(AC-mix) mechanism during feature fusion to improve small target detection capability.Experimental results demonstrate that the improved model maintains lightweight characteristics while achieving an average detection accuracy of 98.9%, representing 3.2%  improvement over the baseline model. Additionally, it reduces parameters by 43.5% and computational load by 2.0G. This algorithm better meets the computational requirements for HDPE fishing vessel welds defect detection in industrial production environments.

In view of the current problem that the detection and identification of artificial reefs in marine ranches is mainly based on manual identification, which is inefficient and costly, the CSF and Ransac filtering algorithms were studied and analyzed in the identification of artificial reefs in multi-beam point cloud data. First, the principles and configurations of the two algorithms were introduced. The data point clouds of the two test areas were collected by the NORBIT iWBMS multi-beam echo sounder. Then, the reef extraction experiments were carried out in the two reef areas, and the recognition accuracy and completeness of the CSF and Ransac algorithms were compared. The results showed that both algorithms had good recognition effects on artificial reefs. The accuracy of the artificial reefs automatically identified and extracted by the CSF algorithm was 95.88%, and the completeness was 93.94%, while the accuracy of the Ransac algorithm was 93.48%, and the completeness was 90.53%. However, the three-dimensional morphology of the reefs extracted by the CSF algorithm was more complete, and the complete three-dimensional information of the single reef could be retained. The research methods and results provide a technical route for the identification and extraction of artificial reefs using multi-beam sonar point cloud data, and provide technical support for the scientific evaluation of artificial reefs in marine ranches.

The gravimetric (weight-based) method is widely used for detecting suspended particulate matter (SPM) in aquaculture water. However, it is labor-intensive and time-consuming. To enable rapid and efficient detection, this study focused on the SPM in the aquaculture environment of Scophthalmus maximus. By capturing video footage of suspended particulates in a tank, we developed an automatic detection method based on the Gaussian Mixture Model (GMM) for identifying SPM in water. The results demonstrated that dynamic grayscale processing combined with GMM-based background modeling enabled the extraction of recognizable images of SPM. An intelligent image screening and particle-counting approach was then established. The recognition algorithm was implemented and automated using Python, incorporating relevant image processing libraries. The GMM-based method achieved a detection limit as low as 0.6 mg/L in an industrial recirculating aquaculture system (RAS) for Scophthalmus maximus. Moreover, particle counts obtained through intelligent recognition showed strong correlation with gravimetric measurements (R² = 0.981). To further validate the method, 24-hour continuous monitoring of SPM was conducted, and the relative error between the intelligent detection and the traditional weight method remained below 5%. These results indicate that the GMM-based intelligent recognition approach can reliably and automatically quantify SPM concentration. This method offers advantages such as real-time monitoring, continuity, intuitive visualization, and operational simplicity, showing strong potential for practical application in aquaculture water monitoring.

Accurate real-time fish fry counting is critical in aquaculture to optimize feeding strategies and enhance farming efficiency. However, traditional counting methods face significant limitations due to overlapping high-density fish schools, complex backgrounds, and real-time requirements. To address this challenge, this study proposes an automated fish fry counting method based on an enhanced YOLOv8s model. The study incorporates a Channel Prior Convolution Attention (CPCA) mechanism into the backbone network, dynamically allocating attention weights across both channel and spatial dimensions to enhance feature extraction and target recognition. Additionally, the lightweight counting detection head (EffiCount Head) is designed by integrating coupling design and partial convolution techniques to reduce model complexity and improve inference speed. The results show that the enhanced model achieves a mAP of 98.2% in fish fry counting, a 4.0% improvement over the original model. The model also reduces the number of parameters by 13.6% and increases inference speed by 15.8%. This method demonstrates high precision and robustness in complex backgrounds and high-density scenarios, enabling efficient real-time fish fry counting and significantly improving aquaculture productivity.