At present, fish culture is moving in the direction of precision culture, and fish target detection is an important part of precision culture. Fortunately, the use of deep learning holds promise for fish target detection. However, the existing fish target detection models have the problems of heavy computation and low accuracy.To address the issues of low accuracy and high computational load in fish target detection,a lightweight fish target detection method based on an improved YOLOv8 model was proposed and named YOLOv8-FCW in this study. Firstly,The experimental comparison of MobileNet, ShuffleNet, GhostNet and C2f-Faster show that C2f-Faster has the best performance.Therefore,the FasterBlock from FasterNet was introduced to replace the Bottleneck module in C2f of YOLOv8, reducing redundant computations in the network model. Secondly, the Convolutional Block Attention Module (CBAM) attention mechanism was incorporated to efficiently extract fish body features and enhance the detection accuracy of the network model. Finally, The experimental results show that the loss value and convergence speed of Wise intersection over union (WIoU) loss function are better than Complete intersection over union (CIoU), Distance intersection over union (DIoU) and Generalized Intersection over Union (GIoU).Therefore,a dynamic non-monotonic focusing mechanism WIoU was introduced to replace CIoU, accelerating the convergence speed of the network model and improving its detection performance.In order to verify the detection effect of YOLOv8-FCW on fish, the original model and YOLOv8-FCW were trained and tested on the fish data set.The fish data set consists of 1000 images, which were divided into training set, verification[] set and test set according to the ratio of 8:1:1. Experimental results show that compared with the original model, the improved YOLOv8-FCW model had increased precision by 1.6 percentage points, recall by 5.1 percentage points, and mean average precision(mAP) mAP0.5 metrics by 2.4 percentage points, while the weight and computational load were reduced to 80% and 79% of the original model, respectively.YOLOv8-FCW achieves high detection accuracy and efficiency with very small model volume and low computational cost. The model shows high accuracy and robustness. The research can help breeders accurately calculate the number of fish and provide technical reference for fish target detection.

Biofloc technology (BFT) is a promising technology for the sustainable improvement of aquaculture productivity. In BFT, maintaining an appropriate C:N ratio (10:1 ~ 20:1) by adding a carbon source promotes the growth of heterotrophic bacteria in water, heterotrophic bacterial assimilation of ammonia-nitrogen to bacterial biomass. In practical use, however, BFT system wastewater treatment functions remain limited in aquaculture. Besides, adding organic carbon also produces large amounts of suspended particulate that affects the health of aquatic animal. Therefore, it is necessary to optimize the BFT system wastewater treatment capability by using a variety of technical methods. Here, we introduce the principle of BFT and the problems in its application. The enhanced and combined methods of BFT, principles, application scopes and effects are discussed. We also highlight the effects of enhanced methods on the BFT system, including pollutants removal efficiency, microbial communities, and the growth performance of aquatic animals. The main messages from the result can be summarized as follows. Firstly, the cultivation of nitrifying bioflocs alleviates the need for organic carbon and dissolved oxygen in aquaculture systems, and reduces suspended solids. Besides, BFT reactor can cultivate high concentrations of bioflocs and improve the removal efficiency of nitrogenous compounds. Adding probiotics, microalgae, macroalgae, and substrates to BFT system improves the growth performance and immunity of aquatic animal, as well as enhance the microbial life and decomposition process, increasing the removal efficiency of nitrogen, phosphorus and organic matter. The coupled Integrated Multi-Trophic Aquaculture System, Recirculating Aquaculture System, and Aquaponics System with BFT can not only improve the nutrient use efficiency and production of aquatic animal, but also reduce the operating costs of the system. We also foresee the application of BFT would bring a change in the aquaculture mode in China. The in-depth research and application for an enhanced and combined method of BFT would further promote the sustainable development of the aquaculture industry.

Cutting is a pivotal step in the initial processing of fish products, encompassing beheading, trimming, slicing, and dicing. Traditional metal-knife cutting methods are marred by inefficiency, imprecision, and a propensity for bacterial growth, failing to meet market demands for precision and quality. Additionally, the reluctance of workers to perform manual labor in wet conditions exacerbates labor shortages and inefficiency. The adoption of innovative cutting technologies, complemented by intelligent controls, is thus imperative. This study reviews the advancements and applications of waterjet and ultrasonic knives in sustainable cutting methods within the fish and food industries. It evaluates their respective merits and demerits, noting that waterjets excel in cutting hard-textured fish, while ultrasonic knives are adept at handling the viscous, elastic, and adhesive properties of fish. The abstract further explores the integration of intelligent technologies in fish cutting, such as machine vision for precise cutting paths, simulation technology for adjusting process parameters, and multi-sensor data fusion for decision-making, which could potentially replace human labor. The study also addresses the current challenges and future directions for these technologies, highlighting the potential of artificial intelligence, machine learning, and deep learning to enhance the autonomy and robustness of fish-cutting equipment. By reducing operational and maintenance costs and integrating advanced technologies, the study envisions a future where fish cutting is more automated, intelligent, and capable of producing high-quality products efficiently to satisfy escalating market demands. This research serves as a valuable reference for industry professionals and researchers aiming to innovate in fish product processing, thereby enhancing the automation and intelligence of fish cutting processes.

Passive acoustic monitoring (PAM) technology can obtain acoustic information of aquatic organisms without disturbing or damaging them, and use it for various ecological research and management activities. With the continuous improvement and advancement of shrimp aquaculture technology, shrimp farming density continues to increase, and problems such as low feed utilization and deterioration of water quality seriously constrain the rapid development of shrimp. The use of passive acoustic monitoring technology to achieve an intelligent automatic feeding system to improve feed utilization and alleviate water quality deterioration is the main way to solve the above problems. The article mainly describes the process of shrimp feeding and the factors affecting shrimp feeding behavior, summarizes in detail the research progress and application of passive acoustic monitoring technology in monitoring shrimp feeding acoustic signals, analyzes the advantages of the technology and the challenges faced in the future development, and at the same time, puts forward the trend of the future development of the existing problems, to provide a passive acoustic monitoring technology for shrimp aquaculture in the application and promotion. The results of this study are summarised in the following table.

In aquaculture, precise fish image segmentation is crucial for growth management. However, the intricate underwater environment, plagued by image blurriness and low quality, poses significant challenges to existing segmentation methods, often leading to reduced accuracy and limited generalization capabilities. To address these issues, we propose an underwater fish image segmentation approach based on an improved Segformer model, designated as FT-Segformer (SegFT for brevity). Our methodology meticulously extracts multi-scale features, spanning from fine-grained high resolutions to coarse-grained low resolutions, utilizing a sophisticated four-layered transformer block structure. Within the decoder, a feature pyramid fusion mechanism seamlessly integrates these features, bolstering contextual understanding. Subsequently, transposed convolutions refine the feature maps, restoring their dimensions and amplifying feature learning capabilities. To evaluate the model, we constructed the UAGF (Underwater Aquaculture Goldfish Fishes) dataset, a genuine underwater aquaculture environment dataset featuring ornamental goldfish, and conducted extensive validation experiments thereon. The experimental results demonstrate that SegFT outperforms existing methods across evaluation metrics such as mIoU, mPA, and mRecall, achieving improvements of 1.76%, 0.39%, and 0.19%, respectively. Notably, in terms of mIoU, SegFT surpasses U-Net, PSPNet, HRNet, and Deeplabv3+ by impressive margins of 1.92%, 3.73%, 3.07%, and 3.58%, respectively. This study underscores the remarkable effectiveness and robustness of our proposed method in complex underwater settings, outperforming existing supervised image segmentation techniques in terms of segmentation performance.

To address the issues of solid waste accumulation on the walls of aquaculture chambers in factory ships, low efficiency of manual cleaning, and the inability to clean in a timely manner, a cleaning system suitable for the aquaculture chambers of factory ships was designed. This cleaning system, considering the specific characteristics of aquaculture in factory ships, includes a cleaning device, a bypass device, a transmission device, and a sewage discharge system. Through simulation experiments, the optimal operating speed of the system and the cleaning efficiency of the device under different working conditions were determined. The results indicate that the system's operating speed should be kept below 0.5m/s, and the slower the speed, the higher the cleaning efficiency. Increasing the rotational speed of the brush can significantly enhance cleaning efficiency. It was also found that the number of working cycles of the brush per unit path is positively correlated with cleaning efficiency. When the operating speed is 0.2m/s and the brush speed is 190r/min, the cleaning effect is ideal. A 30° angle for the cleaning scraper effectively removes residual attachments. The study shows that this cleaning system can resolve the cleaning issues during the aquaculture process, with a compact and efficient structure, high work efficiency, and cost-effectiveness, making it valuable for broader application.

Portunus trituberculatus, a crustacean species of significant economic value, is important in aquaculture and seafood processing. Accurate gender classification is critical for optimizing aquaculture strategies and improving operational efficiency in these industries. Traditional manual methods for sex classification are labor-intensive and prone to errors, highlighting the need for automated solutions. This study proposes an automated gender classification method based on deep learning, utilizing the SE-ResNet18 model, an enhanced variant of ResNet-18. The SE-ResNet18 model incorporates the Squeeze-and-Excitation (SE) module and global average pooling, enabling it to selectively emphasize key feature channels. The model was trained and validated on a large dataset of male and female Portunus trituberculatus images, with data augmentation techniques applied to improve generalization. The results show that SE-ResNet18 achieves a classification accuracy of 99.5%, nearly 4 percentage points higher than ResNet-18. Specifically, male crabs were classified with 99.68% accuracy, and female crabs with 99.74%. The model's robustness was tested under varying conditions, confirming its suitability for real-world applications in automated seafood processing and aquaculture. In conclusion, SE-ResNet18 offers a highly accurate and scalable solution for gender classification in Portunus trituberculatus, with the potential to significantly enhance productivity and efficiency in the aquaculture industry.

It’s crucial to obtain the dimensions of fish fry accurately and quickly in aquaculture. Traditional manual sampling and measurement are time-consuming and labor-intensive and cannot meet the demands of smart aquaculture development. A vision-based method for rapid measurement of fish length is proposed for grass carp fry with length distribution from 20 to 100 mm in this paper. It allows for quick and accurate length measurement of test fish fry without reference and in a non-contact manner. Firstly, an RGB-D camera is used to capture depth information and gray images of the target. Those images are processed to segment the target fish from the background. For the case of overlapping fish, concave regions and points are extracted to separate individual fish based on concave points. An improved thinning algorithm is then used to extract the fish skeleton, and key skeleton points are selected. Finally, by combining the image depth information, the three-dimensional coordinates of the skeleton points are transformed，allowing for the accurate measurement of the total length of the fish fry. Experimental result shows that the proposed method achieves an average absolute error of 1.57 mm and an average relative error of 4.39% in the length measurement of the test fish. It provides a non-contact measurement method that supports applications such as graded feeding and intelligent feeding in the aquaculture industry.

To address the issues of low accuracy and high labor requirements in traditional fish hypoxia detection methods, a hypoxia risk assessment method for cultured fish based on Prune-YOLOv5s has been proposed. This paper introduces a hypoxia risk assessment method for cultured fish based on the Prune-YOLOv5s algorithm. This method firstly collects data on aquatic surface respiration (ASR) performed by fish under hypoxic conditions to create a data set for fish hypoxia. The dataset is then utilized to train the YOLOv5s model. Then, the lightweight and improved YOLOv5s model was used to monitor the behavior of fish surface respiration during hypoxia in real time. The introduction of the ASR coefficient allows for the quantification of ASR instances in fish, which is indicative of hypoxia risk. And the fish hypoxia assessment module is designed to evaluate the risk of hypoxia. The improved performance of the YOLOv5s model before and after modifications and the accuracy of the fish hypoxia assessment module are tested through the fish hypoxia experiment.The test results show that compared with the YOLOv5s model, the detection accuracy, model size, inference speed and detection speed of the PruneYOLOv5s model have been significantly improved. Among them, the detection accuracy of the 65% PruneYOLOv5s model, which has the best comprehensive performance, has been increased by 0.6% compared with the original model. The size of the model is reduced to 45.3% of the original model. The inference speed is improved by 23.8%, and the detection speed is also improved by 31.4%. The fish hypoxia assessment method achieves 97.4% accuracy in the test set of 39 test videos, and has a good performance in the hypoxia cycle experiment. The research indicates that the Prune-YOLOv5s-based hypoxia risk assessment method for cultured fish can effectively detect hypoxic conditions and provide accurate risk alerts, showing high feasibility for practical application.

 To overcome the common issues of color distortion and low contrast in underwater image processing, this study developed an innovative underwater image enhancement technique based on a lightweight non-linear activation free network. The core feature of this technique is the use of multiple cascaded non-linear activation free modules without traditional activation functions, significantly enhancing the flow of information and the efficiency of feature extraction. Additionally, the model integrates an innovative layer attention mechanism, which effectively identifies and optimizes feature dependencies between different layers, enhancing the expression of key information through dynamic adjustment of feature weights. To comprehensively evaluate the performance of the proposed method, detailed experiments were conducted on the Large Scale Underwater Image (LSUI) dataset. Compared with leading models such as FUnIE-GAN and Shallow-UWnet, our model demonstrated a significant advantage in structural similarity index (SSIM), with improvements of 8.17% and 4.13% respectively, markedly enhancing the color accuracy and detail retention of the images. Furthermore, the parameter count of our model was significantly reduced, decreasing by 98% and 50% respectively, greatly enhancing the model's practicality and deployment capabilities in environments with limited computational resources. The results of this study confirm the effectiveness of this enhancement technique in addressing key visual challenges in underwater imaging and also demonstrate its potential for application in extreme visual environments. By introducing this lightweight and efficient image enhancement approach, new pathways have been opened for the further development and innovation of underwater image processing technologies, laying a solid foundation for the widespread deployment of underwater vision systems in practical applications.

In view of the difficulties and low efficiency of manual shucking of fresh scallop(Patinopecten yessoensis), and the potential impact of traditional thermal treatment on the quality of fresh scallop(Patinopecten yessoensis), a precise shucking method based on instantaneous thermal treatment was proposed for scallop(Patinopecten yessoensis).Temperature is the key factor determining the effect and quality of scallop(Patinopecten yessoensis) shucking by thermal treatment.  In order to explore the precise shucking method of scallop(Patinopecten yessoensis) by instantaneous thermal treatment, the body shape parameters of scallop(Patinopecten yessoensis) were measured to construct a precise steam jet shucking method.By comparing the shucking integrity rate, color difference, juice loss rate, etc. of scallop(Patinopecten yessoensis) under different instantaneous thermal treatment conditions, combined with sensory evaluation analysis, the optimal instantaneous shucking parameters were determined.The results showed that compared with 140°C steam, the opening width of scallop(Patinopecten yessoensis) was larger, the shucking tensile value was smaller, and the shucking integrity rate was higher under the action of 160°C steam, thus determining the heat source temperature of instantaneous thermal treatment as 160°C.The longer the steam spraying time, the smaller the difficulty of scallop shucking, the larger the opening width, and the comprehensive sensory evaluation determined that the steam spraying time of instantaneous thermal treatment was 2s.For scallop(Patinopecten yessoensis) with a shuck length of 110-120 mm, a shuck thickness of 22-25 mm, and a weight of 100-125 g/piece, using steam spraying (160 °C, 2 s), the shucking integrity rate of scallop(Patinopecten yessoensis) was 100%, and the juice loss rate was 0.86%. The study showed that the precise shucking of scallop(Patinopecten yessoensis) by instantaneous thermal treatment could better maintain the quality of fresh scallops, providing a theoretical basis for the automated processing of scallops.

To address common issues in underwater images, such as color distortion and reduced contrast, as well as the limitations of supervised methods that rely on large-scale paired high-quality underwater image datasets, an unsupervised underwater image enhancement method is proposed. This method utilizes a conditional variational autoencoder (cVAE) combined with probabilistic adaptive instance normalization (PAdaIN) and multi-color space stretching techniques to improve the visual quality of generated images while ensuring consistency with the original input images. Furthermore, a multi-scale residual connection module is employed to effectively reduce the transmission of non-essential information, thereby enhancing the model's performance. This approach provides an alternative to traditional methods that rely on reference images for training.
Experimental results demonstrate that this method achieves a 12% and 3% improvement in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) on the test set compared to FunieGAN and Water-Net, respectively, significantly enhancing the visual quality of the enhanced images. Moreover, the method exhibits excellent performance across different test sets, demonstrating its robust generalization capability. The study indicates that, without the need for reference images, this approach significantly improves underwater image quality, effectively enhancing image detail and color correction, and provides a viable solution for applications in aquaculture and marine monitoring.

With the rapid development of scallop farming in China, the country has become an important global producer of scallops. However, China's scallop fishing technology is relatively backward, presenting issues such as low efficiency, a high amount of broken scallops, and ecological damage to the seabed. Therefore, developing environmentally friendly fishing techniques and equipment is crucial for promoting the construction of scallop aquaculture-type marine pastures. This research reviews the main scallop fishing techniques and equipment both domestically and internationally, including sweeping, digging, anti-misoperation, hydrodynamic, and suction fishing methods, along with supporting mechanized shellfish unloading devices. Through comparative analysis, it evaluates the advantages and disadvantages of these technologies in terms of basic research, fishing efficiency, structural design, and ecological impact, as well as their practical application effects. Sweeping fishing is suitable for large-scale and rapid harvesting but causes significant ecological disturbance to the seabed and is prone to mistakenly capturing non-target species. Digging fishing is appropriate for collecting scallops on hard substrates, reducing environmental damage, though its efficiency is relatively low. Anti-misoperation fishing reduces the bycatch rate of non-target species, protecting marine biodiversity, but it comes with higher costs. Hydrodynamic fishing utilizes water flow to guide scallops into nets, minimizing seabed ecological impact, yet it requires precise control of water flow parameters. Suction fishing is suitable for shallow sandy substrates with minimal ecological impact but remains in the experimental stage. This article summarizes the problems in China’s scallop fishing technology and proposes future development directions. It advocates for in-depth exploration of the impact mechanisms of fishing on underwater ecosystems to provide theoretical support for reducing ecological impacts. Developing new materials and technologies to optimize existing net designs, such as using biodegradable materials and smart fibers, can reduce resource consumption and pollution. Integrating machine vision, texture simulation, and multi-sensor data fusion can achieve real-time monitoring and intelligent decision-making, improving fishing efficiency and ensuring operational safety. Through technological innovation and upgrading, the goal is to achieve coordinated development between fishery production and environmental protection, thereby promoting the healthy and sustainable development of China’s scallop industry. This research not only provides valuable reference for relevant researchers and enterprises but also offers scientific basis for policymakers to jointly advance the modernization of scallop aquaculture-type marine pastures.

The crayfish industry, centered on Procambarus clarkii, is rapidly expanding but faces challenges due to insufficient automation. Traditionally, manual visual inspection to assess the size and integrity of crayfish during breeding and processing is labor-intensive and susceptible to errors. This paper introduces an advanced algorithm using the YOLOv8n framework to intelligently recognize and grade crayfish by accurately identifying the body, tail, and claws of Procambarus clarkii. The proposed method innovates by replacing the conventional loss function with the CIOU (Complete Intersection over Union) and substituting it with the MPDIOU (Modified Perfect Dark Intersection over Union).A novel scale factor, 'ratio,' is integrated to regulate the size of the auxiliary bounding box in the loss calculation. This modification, when synergized with the MPDIoU loss function, significantly bolsters the precision and efficiency of bounding box regression. Consequently, this leads to the accurate identification of the crayfish's distinct body parts, which is a critical step towards automating the grading process. Empirical evaluation showed significant improvements in recognition rates. The integration of Inner-MPDIoU into the YOLOv8n model enhanced the mean Average Precision (mAP) from 83.7% to 90.8% at IOU thresholds from 0.5 to 0.95.This advancement not only streamlines the grading process but also paves the way for more nuanced and automated sorting systems in the crayfish industry. The study's findings underscore the efficacy of the proposed algorithmic model in accurately identifying key components of Procambarus clarkii. This research contributes to the broader objective of achieving intelligent and precise grading within the crayfish sector, potentially revolutionizing traditional methods and bolstering industry efficiency. The implications extend beyond mere automation, offering a foundation for future research into intelligent systems that can be tailored to the specific needs of the crayfish industry.

The characteristics and sedimentation features of tail water discharged from tilapia farming are the key factors influencing the design of sedimentation ponds and also the precondition and foundation for achieving efficient treatment of tail water. Taking the tail water from tilapia farming in Hainan as the research object, through conducting pollution monitoring and static sedimentation experiments on the farmed discharged tail water and the surrounding environment, the concentrations of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (CODMn), and suspended particulate matter (SS) in the water body were determined, and the pollution situation in the tail water and the reasonable sedimentation time for tail water treatment were analyzed and summarized. The results indicated that the concentration ranges of TN, TP, CODMn, and SS in the tail water from tilapia farming were 2.00 - 15.60 mg/L, 0.59 - 2.74 mg/L, 17.20 - 20.40 mg/L, and 2.50 - 144.00 mg/L, respectively; the concentration ranges of TN, TP, CODMn, and SS in the surrounding water of the culture ponds were 2.20 - 5.80 mg/L, 0.44 - 3.21 mg/L, 4.05 - 18.95 mg/L, and 55.00 - 152.00 mg/L, respectively; the average proportions of particulate nitrogen (TPN) and phosphorus (TPP) in the tail water were 20.80% - 62.80% and 50.80% - 74.90%, respectively. After 24 hours of static sedimentation treatment of the tail water, the decrease rate of SS in the upper water layer reached 83.08%, and the SS concentration decreased by 84.62% after 48 hours of static sedimentation treatment, and the sedimentation effect tended to be stable. Considering comprehensively, the static sedimentation time of the tail water can be selected as 24 hours, which can effectively remove the suspended pollutants in the water body. 

 Whether in the wild or in captivity, large yellow croaker (Larimichthys crocea) may suffer from starvation. To study the effects of starvation and restoration feeding on large yellow croaker, The experiment was conducted with a sample of large yellow croaker with an initial body weight of (122.62±11.08) g and a body length of (17.90±1.04) cm. The fish were starved for0,2,6,10 or16 d (S0,S2,S4,S8,S16) and then feeding was resumed. The results are as follows: the body weights of the starved groups showed a decreasing trend, and the largest decrease was observed in group S8. After resumption of feeding, all the starved groups showed only partial compensatory growth capacity at the end of the experiment , among which the S8 and S16 groups showed lower compensatory growth capacity. Serum glucose, triglyceride, alkaline phosphatase (AKP) and acid phosphatase (ACP) tended to decrease with the extension of starvation time, and reached the lowest at 8 days of starvation. At the end of the experiment, its indicators did not recover significantly after 8d of refeeding.. In addition, cortisol concentration and superoxide dismutase (SOD) activity rose to the highest at 8 days of starvation, while the recovery was not obvious after refeeding. The intestinal tissue structure of large yellow croaker was basically consistent with its physiological and biochemical trends, and the height of intestinal villi and the thickness of muscularis propria decreased to the lowest level at 8 days of starvation, and the recovery was not obvious after re−feeding. IL-1β, IL-10 and TNF-α gene expression in the intestine increased with starvation, was highest in the S4 group and decreased after resumption of feeding. The results showed that: In summary, under the conditions of this experiment, starvation stress lasting for 8 days would have a significant effect on large yellow croaker. Therefore, in order to ensure the feeding effect, it was recommended that the length of starvation of the large yellow croaker should be controlled within 8 days..

Ensuring the appropriate dissolved oxygen concentration in the aquaculture water is crucial to the healthy growth of cultured fish. In order to meet the oxygen demand requirements for high-density aquaculture with super large water capacity of aquaculture vessel. According to the double membrane theory and Fick's law, as well as the testing standards for the oxygenation capacity of aerators, a Speece Cone size model was designed and its oxygen enhancement performance tests were conducted. The experiment results show that the dissolved oxygen concentration at the outlet of the Speece Cone reaches 40 mg/L, and the supersaturated dissolved oxygen is close to 400%, achieving ultra-high saturation dissolved oxygen. Under the conditions of an oxygen intake rate of 7.6 m3/h and an oxygen cone operating pressure of 0.07 -0.25 MPa, the oxygenation capacity is not significantly affected by the operating pressure. However, the optimal intake rate range is significantly affected by the operating pressure. The higher the operating pressure, the less likely it is to form large bubbles on the water surface when the oxygen intake rate increases, thus maintaining a higher oxygen utilization rate. When the oxygen intake is greater than 6.5 m3/h and the operating pressure is between 0.07 MPa and 0.25 MPa, each aquaculture cabin is equipped with one Speece Cone of this structural size to meet the oxygen demand of the aquaculture cabin. The oxygenation capacity of this Speece Cone is significantly higher than other oxygenation devices, and its power efficiency is also higher than most oxygenation devices，which is suitable for oxygenation of aquaculture vessel. This study provides basic data for the engineering design and stable and efficient operation of the oxygen enrichment equipment of aquaculture vessel.

The nutritional components in Antarctic krill undergo varying degrees of degradation during the cooking process due to the influence of cooking conditions. In order to explore the effect of separated water cooking conditions on the stability of astaxanthin in Antarctic krill, the raw materials of Antarctic krill were thawed using flowing water, and then drained and placed in a constant temperature environment for separated water cooking. In the experiment, different cooking temperature and time parameters were set. Firstly, five different cooking temperatures of 60, 70, 80, 90, and 100 ℃ were selected to investigate the effect of heating on the stability of astaxanthin. Secondly, five different cooking times of 1.0, 3.0, 5.0, 7.0, and 9.0 minutes were set at each cooking temperature to investigate the effect of time factors on the stability of astaxanthin. Observe the relationship between changes in astaxanthin content and degradation rate by extending or reducing cooking time. A comprehensive analysis was conducted on the changes in moisture content, loss rate, and astaxanthin retention rate of Antarctic krill under different cooking conditions, and a corresponding astaxanthin degradation kinetics model was established. The experimental results showed that there was no significant change in the moisture content and cooking loss of astaxanthin at lower temperatures or shorter times. However, as the temperature rises or time prolongs, the retention rate of astaxanthin begins to show a downward trend. Under the experimental conditions, the maximum reduction in moisture content was only 2.06%, and the maximum loss rate during cooking was only 2.08%. Further analysis revealed that under cooking conditions of 60-100℃ and 1-9 minutes, the degradation of astaxanthin followed a first-order reaction kinetics model. The reaction rate constant was between 0.0857 and 0.1985 min-1, and the activation energy of the reaction was 21.62 kJ/mol. The time required to achieve 90% degradation was between 11.60 and 26.87 minutes. It was worth noting that as the temperature increases, the degradation rate of astaxanthin increases, while the half-life and the time required to reach 90% degradation decrease. This indicates that high temperature environments may accelerate the degradation of astaxanthin in Antarctic krill. The degradation of astaxanthin in Antarctic krill can be described by establishing a mathematical model: k=0.002 8 T-0.088 8 (where k represents the reaction rate constant), which has a good fitting effect (R2=0.966 4). In summary, the results of this experiment have important reference value for understanding the stability of astaxanthin in Antarctic krill during cooking. These findings can provide theoretical basis for the processing and utilization of Antarctic krill products, and help develop appropriate treatment methods to maximize the preservation of their nutritional components and quality characteristics.

The injection of grass carp vaccine mainly relies on manual injection, which has some disadvantages such as high labor intensity, low injection efficiency, non-standard injection location and poor injection accuracy, which seriously restricts the development of aquaculture. This paper analyzes the research progress of fish vaccine injection at home and abroad, and formulates the operation strategy of automatic vaccine injection according to the characteristics of grass carp vaccine injection. By means of the head and tail orientation device, the consistent attitude adjustment of the fry was realized. The light vision sensor was used to realize the fast identification of the belly and the back of the fish and the automatic allocation of the injection station. A rotary fishing rod was designed, and the stress and speed changes in the fishing process were simulated by LS-DYNA dynamic simulation. The technology of flexible fixation and rapid injection of fish body was studied, and the automatic injection device of vaccine was designed to complete a series of actions of flexible press, injection and return in sequence. The technical problems such as the inconsistency of body shape, the automatic assignment of station and the speed matching of rotating fish were solved. Based on the above technology, a continuous automatic injection device for grass carp vaccine was designed, and a prototype for automatic injection of vaccine was developed. Through the production experiment, the young grass carp 100 -120 mm long was selected as the object of automatic injection. The results showed that the output of the automatic injection device was about 900 fish /h. The survival rate of juvenile fish within 3 weeks after injection was 98.3%, which was 4% higher than that of artificial vaccine injection. The research results can realize efficient continuous injection of grass carp seedlings, improve the automation level of aquaculture equipment, and have a reference significance for continuous injection of fusiform fish seedlings, with high application value and economic benefits.

The integrity of the netting in aquaculture cages is crucial for maintaining farming efficiency and ecological balance. Damaged netting not only reduces performance but may also lead to the escape of cultured organisms, resulting in both economic and ecological losses. To assess the impact of netting damage on hydrodynamic characteristics and flow fields, and to explore preliminary methods for damage localization and severity assessment to guide the optimization of cage maintenance strategies, a unidirectional fluid-structure interaction numerical model based on ANSYS Workbench has been established. The model simulates the flow field around the netting in the fluid domain, obtaining data such as pressure on the netting surface, which is then transferred to the solid domain to calculate the deformation and stress distribution of the netting under fluid loads, achieving hydrodynamic responses of netting with varying degrees of damage at flow velocities of 0.1 to 1 m/s. The results show that the drag of the netting increases with the square of the flow velocity, and the increase in damage degree leads to a decrease in drag, especially when the damage is significant. The flow velocity decay rate is relatively stable under different damage degrees, indicating that the impact of netting damage on the overall flow velocity decay rate is limited. Under a flow velocity of 0.5 m/s, the influence of damaged netting on the flow velocity decay zone in front of the netting is limited, but damage makes the phenomenon of accelerated flow between the net lines more apparent, causing significant interference with the flow velocity decay zone behind. The overall maximum deformation and maximum stress of the netting do not change significantly with the increase in damage degree, but the stress near the damaged area increases significantly. This study not only enhances the understanding of the hydrodynamic characteristics of the netting but also provides important technical support for the design, performance evaluation, and risk management of aquaculture cages.

To meet the environmental monitoring needs of marine ranching, a physical and chemical indicators and visual monitoring platform for sea cucumber bottom sowing culture environment was developed. By constructing the numerical model of the platform, the structural strength analysis of the platform is carried out. Based on the three-dimensional potential flow theory, the platform motion response model under the coupling condition of wind, wave, and current is established. The AQWA software is used to analyze the variation law of the maximum center of gravity amplitude, and the mooring system optimization is realized by optimizing the position of the guide hole and the length of the mooring line. The research shows that under the condition of ten-level wind and wave when the angles of wind, wave, and inflow are 90 °, 135 °, and 90 °, the motion range of the center of gravity of the platform reaches the maximum and is in the most dangerous state; when the position of the guide cable hole is reduced to 0.5 m underwater, the roll, pitch, and yaw amplitudes of the platform are reduced by 50.6 %, 61.7 % and 43.4 %, respectively. When the cable length is reduced to 70 m, these amplitudes are reduced by 28.4 %, 47.7 %, and 32 %, respectively. The mooring safety factor is 1.89, which meets the design requirements. To further demonstrate the accuracy of the numerical model of the platform, the motion response and mooring force of the platform were measured during the influence of typhoon ' Gaemi'. The test results show that under the coupling of wind, wave, and current, the maximum angles of roll, pitch, and yaw of the platform are − 4.829 °, − 0.934 °, and 0.610 °, respectively. The maximum measured angles at sea are − 6.399 °, − 1.329 °, and 0.774 °; the simulated and measured mooring force coefficients of the platform are 3.63 and 3.92, respectively, which are consistent with the lifting trend of the numerical simulation results. The motion response error is less than 2 °, which verifies the good safety of the platform. It provides theoretical and technical support for the development and application of marine ranching environmental monitoring platforms.

To enhance the accuracy of fine-grained classification of shark populations and address issues such as image interference, insufficient extraction of local key features, and lack of semantic correlation between channels, a DM-BCNN model for fine-grained classification of shark populations based on an improved Bilinear Convolutional Neural Network (B-CNN) is proposed. First, deformable convolution is introduced to replace the feature extraction part of the original model with a DRAM_ResNet network structure, enhancing the model's ability to detect complex and irregular shapes and local structures. Then, the NAM attention mechanism is employed to strengthen the model's ability to identify and extract key features. Finally, a Mutual Channel Loss function is introduced to enhance the semantic correlation between different channels of shark images, allowing the model to capture information from various aspects of the images more comprehensively. The results show that the improved DM-BCNN model achieved a Top-1 accuracy of 96.12%, representing a 2.51 percentage point improvement over the original model. The study demonstrates that the proposed improved model outperforms the original model in fine-grained image classification, making it more effective for fine-grained classification and identification of shark populations.

In order to effectively improve the structural damage and environmental pollution caused by bamboo and wood raft cultivation equipment in Beibu Gulf of Guangxi in the process of long-term use, and improve its ability to withstand wind and waves, the HDPE raft culture equipment was selected as the research object, taking the common 15 m long and 12 m wide single raft in Beibu Gulf of Guangxi as an example. AQWA software was used to establish a hydrodynamic analysis model, and based on frequency domain analysis and time domain analysis methods, mooring length and mooring Angle were taken as response indexes to carry out research on the hydrodynamic characteristics and motion response of raft farming equipment, and obtain the motion response and mooring tension variation rules of various degrees of freedom of raft farming equipment. The results show: Under the condition of level 7th high wind and waves, the motion response and mooring cable tension of raft culture equipment change significantly under different mooring lengths and angles. When the ratio of mooring cable length to water depth l/d =1.08 and the Angle of mooring cable is less than 30°, the motion response and mooring cable tension of raft culture equipment are smaller. The optimum mooring form is determined under this condition. Conclusion: Through theoretical and numerical simulation analysis, parameter comparison and early warning are provided for the structural state and safety of aquaculture equipment under different sea conditions, and theoretical reference is also provided for the better application of HDPE materials to raft aquaculture equipment.

 In response to the Food and Agriculture Organization of the United Nations（FAO）’s “Blue Initiative” and the need for sustainable development of the marine economy, this study aims to reduce the energy consumption and operating costs of a 40m-long ocean-going purse seine vessel throughout its entire operating cycle to to enhance the energy efficiency management technology for ocean-going fishing vessels, this paper relies on the Digital Research and development platform of fishing vessels, based on Reynolds-averaged Navier-Stokes equations (Rans) , using the computational fluid dynamics (CFD) analysis software STAR-CCM + , the resistance of an ocean-going purse seine fishing vessel under different draught and different yaw conditions was simulated and calculated, based on the analysis of the change law of the resistance and the search for the optimal pitch angle under different draught and speeds, the displacement and speed requirements of the purse seine fishing vessels at the key operating points in the complete operating cycle are discussed, the optimal pitch operation manual and the economical selection scheme of the main engine power are established, and the actual energy saving effect of the pitch optimization is verified by comparing the energy consumption of the ship in the positive floating state and the optimal pitch state. The research results provide a theoretical support for the implementation of energy-saving technology of ocean-going fishing vessel pitch optimization.

The present study was conducted to evaluate the efficacy of a pollution isolation device in improving water quality in facility-based soft-shelled turtle (Pelodiscus sinensis) aquaculture. A total of 72 soft-shelled turtles, each weighing approximately 300 g, were utilized in this experiment. Two groups were established: one with a small turtle culture pond equipped with a pollution isolation device (treatment group) and another without the device (control group). Water quality parameters, including ammonia nitrogen, turbidity, chemical oxygen demand (COD), and nitrite nitrogen, were monitored and statistically analyzed. The results indicated that the ammonia nitrogen concentration in the treatment group was significantly higher than that in the control group, reaching a maximum of 108 mg/L. The average change rates for turbidity and COD in the control and treatment groups were 14.2% and 8.4%, and 39.5% and 15.5%, respectively. Over the culture period, the control group discharged 286.59 g of COD, 68.33 g of total phosphorus, and 12.32 g of total nitrogen, whereas the treatment group discharged 92.21 g of COD, 46.01 g of total phosphorus, and 5.07 g of total nitrogen. The study demonstrated that, despite the higher ammonia nitrogen concentration in the treatment group, the pollution isolation device significantly reduced the average change rates of turbidity and nitrite nitrogen and decreased the discharge of COD, total phosphorus, and total nitrogen during the culture cycle. Moreover, extending the water exchange period to 15 days effectively reduced the volume of water exchanged and the emission of pollutants. These findings suggest that the pollution isolation device is effective in isolating pollutants and significantly improves water quality in soft-shelled turtle aquaculture systems.

 With the in-depth development of marine ranching construction, environmental monitoring platforms have been widely used in the monitoring of mariculture environments in marine ranches. In view of the problem of cable breakage and damage that occurs during the deployment and recovery process of the data acquisition pod of the marine environmental monitoring platform used in production due to the influence of wind, waves and currents. A pod anti-rolling device for offshore platforms is designed. Based on Newton's second law, a dynamic model of the pod anti-rolling device under the excitation of rolling and pitching of the offshore platform is established and numerical simulation analysis was carried out using Matlab/Simulink software, and the changes in the swing amplitude of the pod with and without the anti-rolling device were compared. The results show that in the case of having an anti-rolling device, the swing amplitude of the in-plane angle of the pod can be reduced by about 66.9%, and the swing amplitude of the out-of-plane angle can be reduced by about 69%. The average anti-rolling ratio is 67.95%. Based on the existing theory, an experimental prototype was built and tested for verification. The test results show that the pod anti-rolling device has an obvious restraining effect on the swing amplitude of the pod. The swing amplitude of the in-plane angle can be reduced by about 73.5%. The research shows that the pod anti-rolling device has important practical guiding significance for improving the reliability and safety of the data acquisition system of the marine ranch monitoring platform.

With the continuous development and utilization of marine resources, the convergence of modern marine ranching and offshore wind power has become an important development direction for intensive use of sea space and improving the efficiency of marine resource utilization. This paper constructed a tidal-driven three-dimensional dual-nested high-resolution hydrodynamic model of the Bohai Sea based on the FVCOM ocean numerical model, analyzed the hydrodynamic characteristics of the combined structure of the artificial reefs and the wind power foundation, as well as the changes in the hydrodynamic environment around the wind power foundation before and after the artificial reefs placed. By validating for harmonic analysis of M2, tidal current magnitude and direction, the skill assessment parameters are above 0.81, and the model had a high simulation accuracy. The model results showed that the tidal current in the wind power area were characterized by a northeast-southwest reciprocal flow; the artificial reefs can significantly reduce the horizontal flow velocity around the wind power foundation over a wide range, with a general reduction of 8% to 15%, reaching a maximum of 32.1%; there was significant upwelling generated above the artificial reefs, upwelling near the front of the incoming flow from the wind power foundation, and downwelling near the back, so their combined structure can significantly promote vertical mixing of the ocean current. The results of the study can provide a new case reference for the convergence of marine ranching and offshore wind power.

To overcome the technical bottlenecks commonly found in water quality monitoring sensors, such as decreased detection sensitivity and accuracy due to prolonged immersion in water, a lifting and cleaning control device for water quality sensors was developed. This device uses an Arduino UNO R3 as the main control board, combined with the SIM7020 NB-IoT module and HC-05 Bluetooth module. A mobile application was developed based on the Blynk platform, allowing users to monitor and control the device's operation in real-time via smartphone, achieving both remote and local dual-mode control. The sensor bracket is designed as a replaceable structure, enabling flexible configuration and adjustment of the sensor's placement. Through an automated lifting and cleaning mechanism, the device can periodically remove dirt from the sensor probe's surface, ensuring data accuracy and the long-term stable operation of the sensors. pH and dissolved oxygen sensors were selected for comparative testing of this device. The results showed that the average relative error of the pH sensor using this device compared to the standard method pH meter was only 2.13%, while the average relative error without the device was as high as 9.51%, showing a significant difference (P<0.05). The average relative error of the dissolved oxygen sensor using this device compared to the standard iodometric method was only 3.09%, while the average relative error without the device was as high as 10.92%, also showing a significant difference (P<0.05). This device has the advantages of a small size, stable system operation, thorough cleaning of the probe, and wide applicability, providing good potential for promotion and application.

Cruise-type aquaculture on deep-sea aquaculture vessels is an important direction for the development of modern marine fisheries. The article explored the effect of transverse rolling motion on the flow field characteristics in the tank based on FLOW-3D software, and calculated the removal of waste particles in the flow field under different transverse rolling conditions on this basis. The results showed that when controlling the period of transverse rolling, the flow velocity in the aquaculture tank increased slightly as the amplitude of the transverse rolling angle increased from 0° to 2°. When the amplitude of the transverse rolling angle was increased from 2° to 7°, the flow velocity in the aquaculture tank showed a large increase, and the v50% increased from 0.171 m/s to 0.477 m/s; the increase of the transverse rolling angle also made the overall distribution of the flow velocity more discrete. The flow uniformity index of the conditions with the transverse rolling angle amplitude of 2° or less is mainly concentrated in 0.8~0.85, while when the transverse rolling angle amplitude is increased to 3°, the flow uniformity of the aquaculture tank decreases significantly, and the flow uniformity indices of the water in the aquaculture tank are all less than 0.8. When the amplitude of the transverse rolling angle is certain, the transverse rolling period is shortened, the flow velocity in the aquaculture tank increases sharply, and the v50% of the flow velocity of each condition and the other quartiles show a linear growth trend while the uniformity of the flow of the water in the tank does not change significantly. For the removal of waste solid particles in the aquaculture tank, the whole process is divided into two phases with opposite effects when the period of transverse rolling and the properties of particles themselves are certain: From the time when the particles were released into the flow field to the 1000th s after the release, the smaller the amplitude of the transverse rolling angle was, the higher the removal rate was; thereafter, until the end of the calculation of the whole example, the larger the transverse rolling angle was, the higher the removal rate of the particles was. The period of transverse rolling had no effect on the removal of particles in the aquaculture tanks, and the time to reach the standard for particles removal was very close for each condition.

 Fishing sonar utilizes sound waves to detect the distribution and activities of underwater fish schools. It typically employs either omni-directional transmission or directional scanning transmission methods for preliminary detection of the position of fish schools. However, there are several limitations with these traditional methods, including poor signal directivity in omni-directional transmission and uneven beam coverage in directional scanning transmission. To address these challenges, this study proposed an improved method based on rotating sector scanning, which aims to optimize the detection performance and meet the specific requirements of fishery sonar in practical fishing scenarios. This study used acoustic simulation methods to systematically analyze the factors that influence the scanning transmission performance, such as the number of sectors and the boundary angles between sectors. The performance of the proposed method was further validated through pool tests conducted under controlled laboratory conditions. The simulation results showed that the use of a four-sector rotating sector scanning transmission method effectively improves the signal directivity while maintaining a full 360° horizontal scan coverage. Specifically, the main lobe intensity was increased by 8 dB when compared to omni-directional transmission, and the maximum intensity difference within a single sector’s transmission beam was reduced to less than 1.5 dB, with the beamwidth difference kept within 2°. These results demonstrated the advantages of the proposed method in enhancing the detection capabilities of the sonar system. To further evaluate the practical performance of the system, laboratory pool tests were carried out to measure the transmission directivity between two adjacent sectors. The results from these tests showed that the proposed method improves the directivity by approximately 8 dB on average over a 64° range in front of the two sectors compared to the omnidirectional transmission. This finding suggests that the proposed design not only enhances the efficiency and accuracy of fish school detection but also helps to obtain accurate fish information in a wider range.

With the rapid development of intensive culture of shrimp, the pursuit of high yield coupled with excessive feeding leads to the aggravation of NH4+-N, NO2−-N and sulfide pollution in the aquaculture water, which seriously harms the growth and physiological function of shrimp. This study used heterotrophic nitrification-aerobic denitrifying bacteria (HHVEN1 and SDVEA2) and sulfur-oxidizing bacteria (GHWS3 and GHWS5) screened from shrimp aquaculture environment to successfully construct two efficient denitrification/desulfurization bacterial consortia NS1-1 and NS2-2, and explored their denitrification and desulfurization capabilities. The results showed the NS1-1 consortium displayed removal rates exceeding 95% for NH4+-N, NO2−-N and sulfide, and NS2-2 exhibited removal rates of 90.27%, 97.38%, and 89.62%, respectively, all of which were superior to a single strain. The bacterial consortia NS1-1 and NS2-2 can exert maximum denitrification/desulfurization efficiency under the culture conditions of temperature 20~35 ℃, carbon nitrogen ratio (C/N) 5~20, salinity 20~30, and utilizing glucose or sodium succinate as the carbon source. The optimum pH ranges of the two consortia were different, with NS1-1 favoring 7.5~8.0 and NS2-2 preferring 7.0~8.5. The bacterial consortia NS1-1 and NS2-2 had good coexistence among various strains in aquaculture tailwater. NS1-1 and NS2-2 displayed effective removal rates of 78.75%, 75.09%, 79.61%, and 81.44%, 62.68%, 72.64% for NH4+-N, NO2−-N and sulfide in practical aquaculture tailwater, respectively. The research shows that the bacterial consortia NS1-1 and NS2-2 have high-efficiency denitrification/desulfurization removal efficiency and exceptional environmental adaptability, providing scientific support for eliminating nitrogen and sulfur pollution in the aquaculture environment.

The sea cucumber industry is a pivotal sector in China's fishery economy, yet the insufficient mechanization level in aquaculture and fishing has become a critical bottleneck. This study systematically reviews the technological advancements in sea cucumber aquaculture and fishing equipment. It first analyzes the technical features of automatic feeding systems and automatic pool transfer machines in industrialized aquaculture, while comparing the efficiency, advantages, and limitations of various fishing methods and operational mechanisms in marine operations. Subsequently, the study highlights key technological innovations, elucidating how kinematic simulation technology optimizes equipment structures and demonstrating how underwater target recognition enhances fishing precision. Finally, three strategic development pathways are proposed: optimization of critical components in engineering aquaculture equipment, integrated innovation in intelligent fishing technologies, and establishment of equipment evaluation frameworks. The findings provide theoretical foundations and technical references for advancing automation and intelligence in sea cucumber aquaculture and fishing equipment.

For factory farming environments characterized by relatively limited computing resources, enhancing identification speed and reducing model size while ensuring high accuracy has emerged as an urgent technical challenge. This necessitates the optimization of both operational efficiency and model scale to accommodate various constraints inherent in practical applications, all while maintaining a high level of accuracy. Traditional object recognition algorithms, such as those within the YOLO family, although demonstrating excellent performance across multiple application scenarios, typically depend on powerful computing platforms capable of supporting their extensive model parameters and complex computational processes. The substantial computational demands present a significant bottleneck for factory farming settings where equipment is often ill-equipped to manage 
large deep learning models; yet real-time monitoring and rapid response are essential for effective management of fish growth and health. To address this issue, existing research into lightweight models offers potential solutions. However, these approaches frequently compromise a certain degree of recognition accuracy in their pursuit of smaller model sizes and reduced computational costs. In the context of the factory farming industry, such declines in accuracy are unacceptable since precise fish monitoring directly impacts both farming effectiveness and economic viability. Consequently, there is a pressing need to explore innovative methods that can significantly reduce model complexity without substantially affecting recognition performance. In light of this necessity, we propose an innovative lightweight fish school identification model named FasterYOLOv9-Slim. This model aims to achieve efficient operation alongside a lightweight design while ensuring high precision through a series of targeted improvements and optimizations.Specifically, the FasterYOLOv9-Slim model is based on the improvement of YOLOv9 and FasterNet, aiming to solve the balance between recognition accuracy and speed through a series of innovative network architecture optimizations. First, on the basis of YOLOv9 model, FasterNet is introduced as a lightweight backbone network to replace the complex backbone structure used in the original model. The core of this change is to take advantage of the more efficient network structure of FasterNet to effectively reduce the large number of parameters and computational requirements brought by the traditional convolutional operation of the YOLOv9 backbone network, which not only significantly improves the detection speed of the model, but also reduces the burden of the model while maintaining a good recognition ability of the target, ensuring the practicality and effectiveness of the model in practical applications. Secondly, in order to further optimize the overall performance of the model, high-dimensional detector head pruning (HDPrune) is used to prune a pair of high-dimensional detector heads in the head network of the YOLOv9 model to reduce the depth of the network to reduce the amount of computation, which not only helps to speed up the operation of the model, but also effectively reduces the accumulation of interfering information in the network to ensure the stability of the model in the face of complex background. The model is stabilized in the face of complex background. Finally, the original feature fusion module RepNCSPELAN4 is improved based on partial convolution (PConv) to obtain a lighter and more efficient version of FasterRepNCSPELAN4, and combined with the advanced downsampling modules ADown and DownSimper, the neck network structure of the model is redesigned to construct a more efficient feature fusion framework. fusion framework. While reducing the computational amount, the network's ability to express features at different scales is enhanced, and the model's efficient coordination between feature extraction, fusion, information transfer, and detection head output is realized.To fully verify the effectiveness of the improvement, ablation and comparison tests are designed. In the ablation test, the effects of three key improvements, FasterNet, high-dimensional detector head pruning (HDPrune), and DFA-Neck, on the model performance are tested, and the results show that FasterNet effectively reduces the number of parameters in the model, proving its significant advantage in reducing the consumption of computational resources, while HDPrune plays an important role in weakening the accumulation of interfering information in the network, and DFA-Neck plays an important role in weakening the accumulation of interfering information in the network. plays an important role in this regard, and DFA-Neck successfully coordinates the functions of FasterNet and HDPrune in the overall network, ensuring the model's high efficiency in the process of feature extraction and information transfer. In the comparison test, the model was compared in detail with the advanced identification models of the same size in the YOLOv7, YOLOv8 and YOLOv10 series models in terms of performance, and the results showed that FasterYOLOv9-Slim achieved 34.14%, 64.02% and 22.22% significant reductions, and demonstrates excellent overall performance in terms of model size, inference speed, and recognition accuracy in comparison with state-of-the-art lightweight networks such as ShuffleNet, MobileNet, and RepViT. The study verifies the effectiveness of the FasterYOLOv9-Slim model in balancing accuracy and speed in the fish identification task under factory farming conditions with limited computational resources, and also provides valuable experience and guidance for the design and optimization of the model in similar application scenarios in the future.

The long-line abalone culture system in the bottom waters outside the bay is a new type of aquaculture facility, which utilises the attenuation effect of waves underwater to achieve better resistance to wind and waves. Currently, most of the research on longline aquaculture systems is aimed at the sea surface, and the research on the seabed is still pending. Therefore, in order to ensure the survival rate and structural safety of the abalone culture system, it is of great significance to study the dynamic characteristics of the long-line abalone culture system in the bottom waters. Based on the similarity criterion, a model test was carried out in the wave current flume laboratory; based on the centralised mass model, a numerical simulation method of the dynamic response of the long-line abalone culture system in the bottom waters was established, and the accuracy of the numerical simulation method was verified; in view of the actual sea conditions off Luoyuan Bay, Fujian Province, the dynamic characteristics of the long-line abalone culture system in the bottom waters were numerically simulated under the action of different waves and currents to analyse the effects of the wave heights, current velocities and flow directions on the stresses on the abalone culture system and the structural safety of the system. The effects of wave height, current speed and flow direction on the force and trajectory of the system were analysed. The results show that: the numerical model is consistent with the model test results, the maximum relative error of the maximum cable tension measured against the waves is 8.33%, the minimum relative error is 4.84%, and the average relative error of the peak horizontal and vertical motions of the float A is 5.23% and 8.92% respectively, which proves that the numerical simulation method used in this paper is accurate and reliable; there is a significant difference in the distribution of the force of the longline and the anchor rope, and the force curve of the longline shows an arch of the force. The force distribution of the longline and the anchor rope has obvious differences, the force curve of the longline shows an arch shape and a sawtooth shape, and the maximum force section is not on the anchor rope, but located in the middle region of the longline; the wave height has a linear relationship with the maximum force of the system and the motion amplitude of the abalone cage, i.e., the higher the wave height is, the larger the transverse and vertical motion amplitude of the system is, and the larger the maximum tension in the middle part of the longline, the supporting rope and the anchor rope is, and the peak tension curve of the supporting rope shows a sawtooth shape; the maximum tension of the anchor rope in the headward side appears nonlinearly when the velocity of the current is increasing, the maximum tension of the anchor rope in the headward side appears nonlinearly. When the flow velocity increases, the maximum tension of the anchor rope on the headwater side shows a nonlinear steep increase, the maximum tension of the longline on the headwater side increases slowly, the maximum tension of the anchor rope and the longline on the backwater side decreases significantly, while the maximum tension of the branch ropes does not increase significantly with the flow velocity, and the maximum tension of each branch rope is the same; when the flow direction increases, the maximum tension of the system's anterior part of the longline and the anchor rope decreases, and that of the posterior part of the longline and the anchor rope increases, and the branch ropes' tensions basically remain relatively stable. The results of this study can provide a reference for the structural design of similar long-line abalone culture system in the bottom waters.

The technology of waterless live transportation of aquatic products has the advantages of low carbon environmental protection, safety and high efficiency. it is more important, it conforms to the current development trend of e-commerce, meets the needs of consumers in various places for fresh aquatic products, and facilitates the implementation of the express delivery service of live aquatic products. In order to prolong the survival time and improve the survival rate of Verasper variegatus, the purpose of long-distance transportation to fully realize the economic value of fresh V. variegatus was achieved in the research process of waterless transportation technology of V. variegatus. In the long-term research on the technology of waterless preservation of aquatic products, the research group found that fasting and cold acclimation of fish before preservation and transportation can effectively prolong the survival time and increase the survival percentage of fish during transportation. At the same time, the preservation effect is affected by aquatic products, development degree, season, growth water area and low temperature tolerance. In this study, cold acclimation, vitamin C addition, anesthetics and biorhythm regulation were used to treat V. variegatus before waterless transportation. The optimal treatment method was screened with the survival time and survival rate as evaluation indexes. The experimental results showed that []in the process of cold acclimation, the survival rate was 83.33%, which was twice as high as that without cold acclimation, when the cooling rate was 1 ℃/h and the water was not transported for 24-36 hours. Based on cold acclimation, supplemented with 300 mg / L vitamin C and 100 mg / L anesthetic MS-222, the 24-hour survival rate increased to 100%. In addition, the survival rate was 50%, which was 200% higher than that of the total dark group, supplemented with 500 Lux light intensity and 48 hours of waterless transport. The experiment shows that cold acclimation, addition of vitamin C, anesthetics and biorhythm regulation can effectively reduce the stress response of V. variegatus during transportation, reduce fish damage, prolong the survival time, and improve the survival rate of transportation, which can meet the requirements of convenient, green and efficient commercial transportation of V. variegatus. Among them, the 500 Lux light intensity treatment method was used to transport the best survival rate of V. variegatus.

With the development of deep learning technologies, object detection has become an important task in computer vision and has been widely applied in various fields. The YOLO (You Only Look Once) series of models, known for their efficient and fast inference capabilities, have become mainstream in the field of object detection and are widely used in various domains. In the aquaculture industry, diagnosing and treating fish diseases is crucial for preventing the spread of diseases and reducing economic losses. To address the problem of bacterial disease detection in freshwater fish, this paper focuses on the application of the YOLOv8 model in object detection and explores the role of data augmentation in improving model performance. Firstly, the basic principles and architecture of YOLOv8 are introduced, and the improvements of this model over previous YOLO versions are analyzed in detail, including the advantages of its network structure and optimization algorithms. Next, this paper proposes a fish disease detection method based on an improved YOLOv8 algorithm. This method incorporates the EMA (Efficient Multi-Scale Attention) attention mechanism into the backbone network, which not only enhances the feature extraction capability but also improves multi-scale feature extraction and cross-space learning architecture. This innovation reduces computational complexity while maintaining high-precision feature representation. Additionally, the GSConv (Grouped Shifted Convolution) operation is adopted in the Neck layer to replace traditional convolution operations, which reduces model complexity and further enhances detection speed without compromising accuracy. Experimental results show that this method achieves a 2.1 percentage point improvement in detection accuracy on our self-built freshwater fish disease dataset compared to the original YOLOv8 model. It also demonstrates significant performance improvement over other existing models. This method can be applied to fish disease detection and prevention scenarios, providing technical support for fish disease detection.

The aim is to explore the best extraction technology of gelatin from black carp skin in order to improve the additional output value of black carp. Black carp is one of the four indispensable fish in traditional freshwater culture in China. It is rich in high-quality trace elements such as protein, phosphorus, selenium, iodine, iron and calcium, which are beneficial to human health. However, at present, in the processing of black carp, except fish products, black carp skin as a by-product has not been fully developed and utilized, and its potential economic value has not been fully tapped, resulting in a great waste of resources. Black carp skin is rich in collagen and is an ideal raw material for extracting gelatin. In this work, the effects of ultrasonic time, enzyme dosage, enzymolysis time and enzymolysis temperature on the extraction yield of gelatin from black carp skin were systematically studied by single factor experiment and Box-Behnken experimental design. Gelatin was extracted from black carp skin by ultrasonic treatment and enzymatic hydrolysis. The influence range and trend of each process were preliminarily determined by single factor experiment. Based on the results of single factor experiment, ultrasonic time, enzyme dosage, enzymolysis time and enzymolysis temperature were selected as key process parameters, and Box-Behnken experimental design was carried out. Through regression analysis, a regression model was established to predict the extraction yield of gelatin, and the optimum extraction conditions were determined. The results showed that the optimum extraction conditions of black carp skin gelatin were ultrasonic time 125 min, enzyme dosage 0.22%, enzymolysis time 154 min and enzymolysis temperature 45℃. Under this condition, three parallel experiments were carried out, and the average extraction yield of gelatin from black carp skin was 18.28%, and the relative error between it and the predicted value was only 0.22%, which indicated that the constructed regression model had high reliability and accuracy. In addition, the extracted gelatin was characterized by FTIR. The results showed that the extracted substances had the basic characteristics of gelatin, such as amide A band, amide I band, amide II band and amide III band, which further confirmed the gelatin properties of the extracted substances. This study provided a technical reference for the development and application of black carp skin resources in gelatin and related products. By optimizing the extraction process, the high-value utilization of black carp skin can be realized, which contributed to the sustainable development of aquatic processing industry. At the same time, it also provides reference and enlightenment for the development and utilization of other fish skin resources.

In aquaculture, the accumulation of leftover feed on the water surface not only leads to wastage but also contributes to deteriorating water quality, significantly impacting the well-being and growth of aquatic organisms. Conventional detection methods face challenges in accurately identifying small feed particles due to the intricate nature of aquatic environments. To tackle this issue, this research introduces an enhanced algorithm based on YOLOv8n for detecting residual feed on water surfaces.This algorithm improves the precision of detecting small feed particles by incorporating a specialized detection layer tailored for small targets. By amalgamating shallow and deep feature information, the algorithm enhances the network's ability to perceive targets across various scales, thereby boosting the accuracy of detecting small feed particles. Furthermore, the integration of the C2f_Faster_EMA module reduces model parameters, elevates detection speed, and fortifies the extraction of intricate features. Additionally, the devised ICBAM module bolsters the amalgamation of feature information for small targets, significantly enhancing detection accuracy.Experimental findings illustrate that the enhanced algorithm delivers exceptional performance across multiple evaluation metrics. Comparative to the original YOLOv8n, the @0.5, precision, and recall rates have surged by 10.3%, 7.6%, and 10.2%, respectively. Furthermore, the algorithm achieves a detection speed of 125 frames per second FPS, meeting the demands for real-time detection. These outcomes underscore the algorithm's capacity to swiftly and accurately identify residual feed on water surfaces, providing substantial technical backing for the intelligent administration of aquaculture.The implementation of this algorithm holds promise in efficiently curbing feed wastage, enhancing water quality, and amplifying the profitability of aquaculture operations. This advancement positions the aquaculture sector on a trajectory towards a more sustainable and efficient future.

Setting 4 different culture densities of Eriocheir sinensis(0, 0.45, 0.68, 0.90 ind•m2) in Dongying, and the changes of water and benthic community were studied. It provides theoretical basis for the construction and optimization of rice-crab co-cropping model in saline-alkali land. The results showed as follows: During the experiment, water temperature (T), dissolved oxygen (DO), total nitrogen (TN) and total phosphorus (TP) were firstly high and then low, which were obviously affected by seasonal changes.There was no significant difference in water indexes among different densities of Eriocheir sinensis(P>0.05), which may be related to the obvious small culture density compared with the cultivated paddy area. The average annual water nitrogen and phosphorus content of rice and crab co-cropping system was significantly lower than that of rice single cropping system. And it is significantly higher than the single culture of aquaculture pond. The nitrogen and phosphorus absorption capacity of the co-cropping system was the highest in these three aquaculture models.Three species of benthic were detected during the experiment, which were Limnodrilus, Chironomid and Cipangopaludina.The main reason is that the sandy sediment of the Yellow River Delta is not suitable for their survival The biomass was positively correlated with water salinity (P<0.05) and negatively correlated with water PO43--P (P<0.01). In conclusion, Rice-crab co-cropping in the improved saline-alkali land has obvious ecological effects.

In order to solve the problem of poor stability and weighing accuracy of the load cell under ship load condition, the effect of ship swaying on the output of four-arm bridge resistive load cell was studied. The mass data of single fish at transverse tilt angle (0°~22.5°) and longitudinal tilt angle (0°~10°) were obtained, and analyzed by multivariate regression analysis on the output mass of the load cell and the tilt angle. A multi-layer BP neural network structure was constructed with the architecture of a 4-layer forward feedback [3-8-1-1] BP neural network, and the BP neural network was used to make predictions and adjust the parameters with a training model so that it could accurately predict the quality of the fish, and the algorithmic compensation method was studied. The results showed that the multiple regression coefficients estimated in the optimal linear equations reached the significant level (P<0.01) for different raw material qualities, indicating that the established linear regression equations had high reliability and good linearity, and the results of regression analysis and finite element analysis were consistent. The BP neural network model was used to construct the compensation method of shipboard instantaneous weighing data, and the actual mass value was predicted by the ship's transverse and longitudinal tilt angle, and the BP model showed good validity, high accuracy and good generalization ability for the change of weighing data under single action of transverse tilt and longitudinal tilt, as well as under the composite action, and the error rate of the BP model was reduced to 0.092% after compensation, which is very close to that of the mass value under the horizontal state, with a low error rate.The results of this research can provide a reference for the weighing of aquatic products under shipboard conditions.