This paper addresses the limitations of traditional water quality detection equipment in aquaculture, such as restricted detection range and insufficient depth, by designing a water quality detection robot and conducting structural dynamics and fluid dynamics analyses to verify the feasibility of the proposed design. First, the robot’s structural design and modeling were carried out, and the electronic cabin was subjected to static strength verification using the finite element method. Second, based on CFD, RANS and RNG k-ε turbulence models were used to analyze the robot's straight-line motion performance at speeds ranging from 0.2 to 1.0 m/s. Finally, overlapping grid technology was applied to explore the robot’s variable-speed motion characteristics. The results show that at a depth of 100 meters, the maximum equivalent stress in the electronic cabin is 50.70 MPa, with a maximum deformation of 0.0763 mm, which meets the pressure resistance requirements. Under straight-line motion, the hydrodynamic coefficients (、、、 and ) were −34.75, −37.54, −82.81, −71.16, and −93.47, respectively. In the variable-speed motion state, the corresponding inertia hydrodynamic coefficients (、 and ) were −7.32, −24.25, and −22.53, respectively. This study not only provides a novel equipment with underwater mobile detection system capable of full-range water quality monitoring,  but also offers data support for the structural optimization and motion control strategies, advancing its practical application in the field of water quality detection.

The height of the mooring point significantly affects the platform's hydrodynamic characteristics and structural stress, thereby impacting its stability. This study uses ANSYS to investigate the hydrodynamic and structural stress features of a semi - submersible aquaculture platform under different mooring point heights. The results indicate that higher mooring points lead to greater platform motion responses, especially in terms of increased tilt angles, which negatively affect platform operation and stability under extreme sea conditions. The lowest mooring point (P1) experiences higher mooring forces and stress but remains within acceptable limits, with the smallest motion response, making it favorable for platform operation and recommended as the ideal mooring point. Further analysis was conducted on the impact of different wave periods and load directions on platform motion and stress. When only subjected to wave action, the mooring force and stress show low sensitivity to the wave period, while horizontal displacement and heave increase with the wave period. Under combined wave - current action, the trends are opposite to those observed under pure wave action. The mooring force is maximized and platform displacement minimized when the load direction is 0°, while at load directions of 15° and 30°, two mooring chains share the load, resulting in reduced mooring forces, heave, and stress, while horizontal displacement increases. The findings provide valuable insights for the design and optimization of mooring heights for semi - submersible aquaculture platforms.

To address issues like seedling blockage and damage during the falling and clamping processes in the development of mechanized kelp seedling clamping equipment, this study measured the biomechanical parameters of suitable - period kelp seedlings (Saccharina japonica) to obtain the mechanical parameters and deformation patterns of their stems and leaves under external forces. The structural composition of kelp seedlings was introduced, and their biological characteristics were measured. A texture analyzer and a friction coefficient analyzer were used to measure the mechanical properties of kelp seedlings, and discrete element simulations using EDEM verified the compression characteristics of kelp seedlings. Results showed that the tensile strength of kelp seedling stems ranged from 0.67 to 0.79 MPa, their shear strength from 1.95 to 2.23 MPa, and their compression recovery from 0.43 to 0.46. For kelp seedling leaves, the tensile strength was between 0.52 and 0.71 MPa, the shear strength between 1.12 and 1.74 MPa, and the compression recovery between 0.03 and 0.06. The tensile and shear strengths of kelp stems and leaves decreased with increasing loading speed, while the compressive recovery increased. Among stainless steel, rubber, and silicone, the friction coefficient between kelp seedlings and silicone was the highest when the normal force and loading speed were constant. The error between the simulated and measured radial compression values of kelp seedling leaves using EDEM was 8.2%. These findings offer data and theoretical support for optimizing the design of kelp seedling clamping equipment.

To address the environmental pollution caused by the untreated discharge of laver processing wastewater and the inadequacy of traditional solid-liquid separation devices due to the unique adhesiveness of residual algae and impurities, a solid-liquid separation device for laver processing wastewater was developed. The working principle of this device was briefly described, and the structural dimensions of its core components were designed and calculated. A prototype was assembled for performance testing. With the separation rate and adhesion rate of laver processing wastewater as evaluation indices, and considering roller brush speed, filter mesh aperture, and backwashing movement speed as factors, a three-factor, three-level Box-Behnken experimental design method was used to conduct a quadratic orthogonal combination experiment. The results indicated that the optimal operating parameters were a roller brush speed of 476.28 r/min, a filter mesh aperture of 0.32 mm, and a backwashing movement speed of 0.007 m/s. Under these conditions, prototype testing achieved a separation rate of 84.12% and an adhesion rate of 4.71%, with no significant precipitates or suspended solids observed in the treated wastewater, meeting the requirements for solid-liquid separation. The study demonstrates that this solid-liquid separation device exhibits good separation performance, significantly reducing the risk of water and soil pollution from direct wastewater discharge and mitigating eutrophication issues, indicating promising prospects for widespread application.

 The high humidity environment in aquaculture workshops reduces the service life of workshop engineering and facilities. The high - humidity environment in aquaculture workshops present a significant challenge to equipment durability, frequently resulting in metal corrosion and mold growth. In particular, most industrial aquaculture workshops are steel - structured. Condensation on the steel frames leads to corrosion of the steel structures, which not only affects the aesthetics and service life of the workshops but also causes pollution to the aquaculture water quality. However, there is a lack of research on the temperature and humidity distribution and condensation under winter operating conditions in actual aquaculture workshops. This study provides a combined temperature - humidity control strategy for aquaculture workshops in winter, reducing the humidity in the workshop and the operating energy consumption of the ventilation system effectively. The accuracy of the numerical model was verified in this study through actual measured data of thermal environment in the experimental platform of aquaculture workshop. Thermal and humid environment and condensation in an aquaculture workshop in the condition of natural ventilation, mechanical ventilation and mechanical ventilation combined with heating are being investigated, and environmental control measures is proposed. The appropriate relative humidity range for aquaculture workshops is 60% to 80%. The study shows that the humidity of workshop and condensation on the inner wall can be reduced in the condition of mechanical ventilation. In the condition of mechanical ventilation combined with heating, indoor temperature can be increased uniformly, humidity can be reduced effectively in the workshop in winter, and heat consumption and operating costs can be reduced. The temperature and humidity monitoring points in the workshop are located at a height of 3 - 4 meters, providing a basis for the temperature and humidity monitoring positions in industrialized aquaculture. Intermittent ventilation for 23 minutes can achieve the effect of low - energy - consumption dehumidification. By adopting total heat recovery of thermal-humid air, through calculating the total heat recovery of mechanical ventilation in the actual workshop in winter, the heat recovery efficiency reaches 71.7%. The conclusions of this study have laid a research foundation for the dehumidification and energy - saving design of the air - conditioning system in industrialized aquaculture workshops, as well as the intelligent control of temperature and humidity.

In order to calibrate the discrete element simulation parameters of fish pellet feed and analyze the influencing factors of repose angle of fish pellet feed, this paper measured the repose angle of fish pellet feed based on stacking experiment. Taking Tongwei 150 model pellet feed as the research object, the funnel method, cylinder lifting method and suction plate method were used to realize the formation of the repose angle, and MATLAB software was used to process the accumulation image to determine the boundary contour of pellet feed. Plackett-Burman test was used to screen out three significant influencing factors: the static friction coefficient between feed and feed, the restitution coefficient between feed and feed, and the rolling friction coefficient between feed and feed. Finally, Box-Behnken test was used to construct the regression model of repose angle and significant influencing parameters. The optimal simulation parameters were determined by Design Expert optimization as the recovery coefficient between feed and feed is 0.28, the static friction coefficient between feed and feed is 0.31, and the rolling friction coefficient between feed and feed is 0.14, among which the static friction coefficient between feed and feed and its quadratic term have the greatest influence on the repose angle of granular feed. The experimental results provide a reference for the discrete element parameter calibration of fish pellet feed and the design of feed delivery and throwing equipment.

The continuous accumulation of nitrogen and phosphorus during zero water exchange biofloc culture is difficult to deal with, while floating duckweed (Lemna minor) is regarded as a plant with good water treatment ability, and in view of its aquaculture benefits as a feed supplement, so in order to better explore the duckweed-biofarming system for P. vannamei, the present study was set up to investigate the floating duckweed group (DG) and control group (CG) for a 45-day experiment with P. vannamei . At the beginning of the experiment, 40 g (wet weight) of duckweed was placed in the net box of GD. Fresh duckweed was fed every day in addition to the normal feed, and the amount of feed was adjusted according to the body weight of P. vannamei measured every week. Results indicated that DG achieved significantly lower levels of nitrate nitrogen (NO3--N), total nitrogen (TN), phosphates (PO43--P), and total phosphorus (TP) by 13.7%, 11.6%, 12.5%, and 11.8%. The growth metrics in DG were all significantly higher than those in CG (P < 0.05). The crude ash, crude fat, and crude protein of the shrimp in DG were (2.37±1.13)%, (2.48±0.35)%, and (19.63±0.64)%, showing significant differences compared to CG (P < 0.05). The levels of ASP, Gly, Met, Leu, Lys, and Arg in DG were also significantly different from those in CG (P < 0.05). The muscle hardness, chewiness, and water-holding capacity of the P. vannamei in DG were significantly higher than those in CG (P < 0.05). The enzyme activity tests indicated that the shrimp in the DG exhibited a significant enhancement in antioxidant and digestive capacities (P < 0.05). Based on these findings, incorporating duckweed in biofloc shrimp systems can purify water and enhance the growth performance, muscle quality, antioxidant capacity, and digestive enzyme activities of P. vannamei.

The phenomenon of oxygen deficiency is common in intensive aquaculture and live fish transportation, and has become an increasingly important issue in the aquaculture industry. The effects of hypoxic stress on water quality, biochemical parameters, and tissue structure during the transport of Siberian hybrid sturgeon (♀Acipenser baerii × ♂Acipenser schrenckii) larvae were investigated. The hybrid sturgeon larvae were placed in environments with dissolved oxygen concentrations of 2.5 ± 0.5 mg/L and 7.5 ± 0.5 mg/L for simulated transportation. Sampling was conducted before transport (0 h) and at 3, 6, 9, and 12 hours post-transport. The results showed that after 12 hours of transport, the survival rate of larvae in the hypoxic group was 54%. The serum cortisol (COR) concentration, liver alkaline phosphatase (AKP) and acid phosphatase (ACP) activities were significantly higher than those before transport (P<0.05). Serum glucose (GLU) levels peaked at 6 hours post-transport. Liver malondialdehyde (MDA), glutathione (GSH) content, and superoxide dismutase (SOD) activity were significantly higher than those in the normoxic control group (P<0.05). The liver antioxidant capacity (T-AOC) and lysozyme (LZM) activity in the hypoxic group exhibited a trend of initial decrease followed by an increase. After 12 hours of transport, the muscle glycogen content in the hypoxic group was significantly lower than that in the normoxic control group (P<0.05), while lactic acid levels peaked at 3 hours. More severe tissue damage was observed in the liver of hypoxic group larvae after 12 hours of transport. The study indicates that hypoxic stress during transportation affects juvenile fish survival rates, water quality, and other parameters, leading to oxidative stress responses and exacerbating liver tissue damage. 

To address the issues of data tampering, low credibility, and high on-chain storage pressure in aquaculture, this study constructs a blockchain-based data storage and traceability model for aquaculture. The model adopts a hierarchical storage strategy, where unstructured data such as videos and images generated in aquaculture are stored in IPFS, with only their hash addresses recorded on the blockchain. Additionally, encryption techniques are integrated to enhance data security. For structured data collected by sensors, a batched on-chain mechanism is designed, and data compression algorithms are introduced to reduce on-chain storage costs. Meanwhile, smart contracts are employed to enable automatic data verification. Using pufferfish aquaculture data as an example, the model is implemented and tested on a Hyperledger Fabric consortium blockchain. The results demonstrate that the proposed blockchain-based data storage and traceability model effectively ensures reliable traceability of aquaculture data. The value density of on-chain data is improved by approximately 91.6%, the transaction throughput reaches up to 300 TPS, and the average transaction latency is 0.5 seconds. These results indicate that the model significantly alleviates on-chain storage pressure and meets the traceability and storage requirements of aquaculture data.

Offshore aquaculture has developed rapidly in recent decades. As the most common aquaculture equipment, fish cages are widely used in the world. In order to further adapt to the harsh environment and improve the efficiency of aquaculture, a variety of large scale aquaculture cages combined with traditional marine engineering structures have also emerged. Vessel-shaped fish cage is a new type of large aquaculture structure, composed of steel floating body, net, steel frames and mooring lines, etc. Different from the conventional platform and fish cage, vessel-shaped fish cage has a large number of nets, which will affect fish cage motion and mooring response, furthermore increase economic and ecological risks. In this study, a time-domain coupling analysis method is developed to calculate the dynamic response of large floating cages in waves. Firstly, the three-dimensional potential flow theory is employed to determine the hydrodynamic coefficients of floating body, including added mass, potential damping coefficients and first order wave excitation force RAO. Subsequently the state space method is used to simulate the radiation load of floating body, the Morison equation is used to solve the hydrodynamic loads on slender structures (including steel frame, mooring line and net). Then, the coupled whole cage motion equation is established in time domain. By numerical method, influence of 3 different net models (no net, rigid net, flexible net) and net solidity ratios on motion and mooring responses were studied respectively. The results show that net increases the surge motion with 26%, but the heave and pitch motion are weakened due to the net damping effect. Additional, damping effect of flexible net is more obvious than that of rigid net. An increase of mooring line tension due to the net influence is also observed. This study can provide engineering reference and design basis for large fish cage in the concept and basic design stage.

Semi-submersible platforms face risks of mooring failure and motion instability caused by the combined action of waves and currents in exposed sea conditions. To verify the hydrodynamic performance of a retractable aquaculture platform, physical model tests were conducted to evaluate its behavior under three typical operational conditions: maintenance, floating, and seabed-resting. A 1:75 scale model was used to measure the platform’s six-degree-of-freedom motion responses and the dynamic characteristics of mooring line tensions. The results show that under maintenance and floating conditions, the combined action of waves and currents leads to the maximum mooring forces, while different load combinations have little impact on the platform’s motion. The wave period significantly affects mooring forces and pitch motion but has a minor influence on linear displacements. Under the combined action of waves and currents, mooring forces and linear displacements increase with wave period, whereas the opposite trend is observed under pure wave conditions. In the seabed-resting condition, the platform’s surplus weight provides strong resistance to sliding and overturning, with negligible motion and mooring forces. A comparison of the platform’s dynamic responses under maintenance and floating conditions reveals that under pure current or combined wave-current conditions, increasing the draft heightens mooring forces and exacerbates pitch motion while limiting linear displacements. In contrast, under pure wave conditions, increasing the draft reduces motion responses and mooring forces. The study confirms that the stability of the six-point mooring system and the strength of the mooring chains meet the design requirements, providing a basis for platform construction.

This study aimed to construct an integrated aquaponics system combining Litopenaeus vannamei and Beta vulgaris var. cicla L, and evaluate its potential for nitrogen/phosphorus removal and resource utilization in aquaculture wastewater treatment. The system's effectiveness was investigated through monitoring water quality parameters (turbidity, NH₄⁺-N, NO₃⁻-N, NO₂⁻-N, TN, TP), assessing plant and shrimp growth patterns, and analyzing microbial community dynamics. Results demonstrated that the system achieved a maximum turbidity removal efficiency of 66.9 %. Peak removal efficiencies reached 68.6 % for NH₄⁺-N and 86.0% for NO₃⁻-N during mid-operation, while NO₂⁻-N concentrations remained stable at 0.10-1 mg/L. Both Beta vulgaris var. cicla L and Litopenaeus vannamei maintained normal growth, with the plants assimilating 20.6% of total nitrogen input and 15.4 % of phosphorus input. Microbial analysis revealed that Beta vulgaris var. cicla L promoted enrichment of functional microorganisms responsible for nitrogen and phosphorus removal. The system exhibited excellent nutrient removal performance mediated by synergistic plant-microbial interactions, providing a novel approach for resource utilization of aquaculture wastewater. These findings highlight the practical significance of shrimp-vegetable symbiosis systems in sustainable aquaculture wastewater management.

In order to study the effects of different water spinach (Ipomoea aquatica) floating bed coverage ratios in aquaponic systems on the feeding rate and growth performance of Nile tilapia (Oreochromis niloticus), four experimental groups were designed: a control group with 0% coverage (C0), and three aquaponic groups with 20% (C2), 30% (C3), and 40% (C4) coverage ratios. Each group contained three replicates. Feeding rates were calculated every 7 days. After 35 days of rearing, growth indices and serum antioxidant parameters were measured. Results showed no significant differences in feeding rates among groups during the 7th days and 14th days (P>0.05). On the 21st days, aquaponic groups exhibited significantly higher feeding rates than the control (P<0.05). On the 28th and 35th days, feeding rates followed the order C4 > C3 > C2 > C0 (P<0.05). At the end of the experiment, all performance indicators in the aquaponic groups surpassed those of the control group,and with the increase of the floating bed coverage rate, the weight gain rate and specific weight gain rate of tilapia in each group increased, the activities of serum antioxidant enzymes (CAT, GSH-PX) enhanced, the total antioxidant capacity (T-AOC) increased, and the content of malondialdehyde (MDA) decreased. Considering the feeding rate and growth performance indicators of tilapia in each group, it can be concluded that the aquaponic group with a 40% floating bed coverage rate had the best rearing effect within the experimental range.

The land-based elevated round pond culture model has garnered significant attention in intensive aquaculture in recent years due to its advantages of land and water conservation and controllable farming environments. To evaluate its impact on the comprehensive quality of fish, this study focused on juvenile hybrid snakehead "Male Snakehead No.1", conducting a 365 day comparative farming experiment between an intensive land based elevated round pond group and a traditional pond culture group. post experiment analyses included growth indicators, muscle conventional nutrients, mineral elements, amino acid composition, and fatty acid content. Results revealed that in terms of growth,the intensive group exhibited higher weight gain rate, specific growth rate, and lower feed conversion ratio compared to the pond group, though differences were not statistically significant (P> 0.05). The intensive group showed a significantly higher crude fat content (P<0.01) and no difference in protein content (P>0.05). The intensive group had significantly higher calcium levels (P<0.05) but significantly lower zinc, selenium, iron, and sodium levels (P<0.05). Amino acid analysis identified 18 amino acids in both groups: the intensive group showed significantly lower total amino acids (TAA), essential amino acids (EAA), non-essential amino acids (NEAA), and umami-associated amino acids (DAA) (P<0.05). However, both groups had EAA/TAA ratios exceeding 40%, indicating high-quality protein. The intensive group detected 16 fatty acids, with significantly higher saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids (P<0.05) than the pond group, including unique components like EPA. The study concludes that land-based elevated round pond culture enhances muscle fat and functional fatty acid content but requires feed fortification to optimize mineral and amino acid balance. These findings provide insights for refining aquaculture strategies and improving aquatic product nutritional value.

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.

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.

 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.

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.

To address the limitations of insufficient feature information of characteristic factors, insufficient mining of complex temporal relationships between multiple factors, and low efficiency of model hyperparameter optimization in shrimp breeding and feeding prediction, a long short-term memory network model based on attention mechanism and genetic algorithm optimization (GA-LSTM-ATTN) was constructed. Firstly, based on the core factors such as dissolved oxygen, water temperature, body length and number of shrimp, the growth rate was introduced as a supplementary feature. Secondly, combined with the attention mechanism, the learning ability of the model to the relationship between multiple factors and the feeding rules at different growth stages was enhanced. Then, the genetic algorithm was used to optimize the hyperparameters such as time step, hidden layer dimension, network depth, number of training iterations and batch size before model testing. The results show that the R² (coefficient of determination) = 0.8683, RMSE (root mean square error) = 0.3703 and MAE (mean absolute error) = 0.3311 on the breeding dataset. Compared with the benchmark LSTM model, the R² is increased by 7.3%, the RMSE is reduced by 15.2%, and the MAE is reduced by 13.5%. Compared with the mainstream prediction models, the prediction accuracy of GA-LSTM-ATTN is also improved. In conclusion, the model can effectively improve the accuracy of shrimp feeding prediction, and can provide technical support for accurate feeding in actual aquaculture.

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.

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.

Oysters hold the top position in terms of production among shellfish farming species, demonstrating significant economic value. However, current farming facilities face challenges such as low levels of standardization and mechanization, as well as fragility to wind and waves. These issues severely limit the sustainable development of the oyster farming industry. In this research, an elevating oyster farming platform was meticulously designed. Subsequently, the physical model test method was employed to comprehensively investigate the hydrodynamic characteristics of the platform under various wave parameters, drafts, and mooring configurations. The research indicates that the motion responses and mooring line forces of the farming platform are positively correlated with wave height and period. In contrast, the growth rates of the motion responses and mooring line forces are negatively correlated with the period. Under identical working conditions, the amplitude of heave and pitch motion changes more dramatically. Under extreme sea conditions, when the farming platform transitions from the floating state to the submerged state, the surge, heave, pitch, and mooring line forces are reduced by 27.32%, 45.89%, 42.32%, and 18.47%, respectively. This transition significantly enhances the platform's capacity of withstanding wind and waves. Notably, the attenuation effects on the heave and pitch motions are the most pronounced. The motion responses and mooring line forces of the slack mooring farming platform are relatively lower than those under the tension mooring condition. Moreover, their increase exhibits an approximately linear relationship. This research not only provides a theoretical support for the development of oyster farming platforms but also offers a critical reference value for the design and research of other shellfish farming platforms.

To accurately predict the air gap performance of offshore frame-type aquaculture platform, a frame-type aquaculture platform was selected as the research object. Based on linear wave diffraction/radiation theory, Morrison equation theory, and rigid body kinematics principles, frequency-domain method was employed to analyze air gap performance. The effects of viscous loads on structural slender members and nettings, disturbed wave surface elevation, position mooring system stiffness on air gap performance were analyzed. Key findings from comparative analysis include: viscous loads on structural slender members and nettings substantially improve platform motions and optimize air gap performance, with a decrease range of about 0.3-1.0 m; Disturbed wave surface elevation significantly affects the air gap performance of frame-type platforms, with an increase range of about 0.2-0.7 m and a decrease range of about 0.3-1.6 m; Position mooring system stiffness constrains platform motion and affects the air gap results, with a decrease range of about 0.1-0.4 m. The results emphasize that the effects of viscous loads on structural slender members and nettings, disturbed wave surface elevation, position mooring system stiffness should be considered reasonably in frame-type aquaculture platform air gap analysis.

To address the defects of traditional vertical flow sedimentators, such as low particle interception efficiency under high hydraulic loads and vulnerability to turbulent interference, this study designed a vortex-type vertical flow sedimentation filter. By integrating the principles of vertical flow sedimentation and cyclone separation, and combining CFD-DPM coupled simulation with experimental verification, the study systematically explored its enhanced removal mechanism for suspended particulates in recirculating aquaculture systems (RAS). Numerical simulations showed that the vortex structure optimizes the flow field distribution through the synergistic effect of centrifugal force and gravity, effectively suppressing particle escape. In comparative tests, under a hydraulic load of 15 m³/(m²·h), the vortex-type filter achieved interception rates of 72.92%±7.40% and 59.24%±5.15% for influent total suspended solids (TSS) concentrations of 25 mg/L and 50 mg/L, respectively, representing improvements of 28.6% and 36.0% compared to traditional devices (P<0.05). However, there was no significant difference in the variation of effluent TSS concentration with increasing flow rate between the two devices (P>0.05). The study demonstrates that the vortex design shortens the hydraulic retention time through a cyclone-enhanced mechanism, significantly improving the stability of particle interception under high-load conditions and addressing the performance degradation of traditional devices caused by increased flow velocity. This achievement provides a solution combining high efficiency and engineering applicability for optimizing solid-liquid separation equipment in RAS.   

In order to solve the problems of large amount of water drainage, deterioration of water quality in the late stage of eel culture, and high investment and operation cost of equipment and facilities, this study optimized a water-saving and emission-reducing aquaculture process for eel and applied it in practical scenarios. An orthogonal experimental design was employed to develop an in-situ water treatment technology using "bacterial granules-composite bacterial liquid agents" based on three selected functional strains with nitrogen and phosphorus removal capabilities. The optimized industrialized water-saving and emission-reducing aquaculture process was then demonstrated through controlled experiments at different stocking densities for fingerling eel over a 120-day culture period. The results showed that in Treatment Group I (500 ind./m³), the harvest size, yield, specific growth rate, and absolute weight gain rate of the fry were significantly higher than those of the control group by 41.4%, 43.9%, 17.3%, and 48.2%, respectively (P<0.05), while the feed conversion ratio was significantly lower by 17.6%(P<0.01). In Treatment Group II (750 ind./m³), the harvest size, yield, specific growth rate, and absolute weight gain rate were significantly higher than the control group by 20.5%, 83.0%, 8.9%, and 23.3%, respectively (P<0.05), with the feed conversion ratio significantly lower by 11.0%(P<0.01). Both treatment groups achieved over 75% water savings and emission reductions compared to the control. In Treatment Group I, the average concentrations of ammonia nitrogen, nitrite nitrogen, nitrate nitrogen, total nitrogen, and total phosphorus in the water were significantly lower than those in the control group by 90.8%, 80.7%, 10.0%, 51.5%, and 38.7%, respectively(P<0.05). In Treatment Group II, these concentrations were significantly lower by 88.0%, 74.2%, 5.6%, 42.6%, and 21.3%, respectively(P<0.05). These findings indicate that this aquaculture process offers advantages such as water conservation, emission reduction, sustained high water quality, and low investment and operational costs, suggesting broad application prospects.

With the increasing reliance of the aquaculture industry on groundwater resources, the water quality issues caused by excessive iron and manganese concentrations in groundwater have become progressively prominent. Elevated levels of iron and manganese adversely affect the respiration, immune function, growth, and development of aquaculture organisms, thereby restricting the widespread application of groundwater in aquaculture practices. In this study, quartz sand and zeolite were chemically modified using potassium permanganate (KMnO₄) and manganese sulfate (MnSO₄) solutions, while physical modification of zeolite was achieved through high-temperature calcination. Comprehensive characterization of the modified materials was conducted. Iron and manganese filtration experiments were performed to investigate the maturation period required for achieving stable iron-manganese removal efficiency in both modified and unmodified quartz sand and zeolite. The results demonstrated that chemical modification induced the formation of spherical particles on quartz sand surfaces and created dense etching grooves on zeolite, whereas physical modification disrupted the layered structure of calcined zeolite. Energy-dispersive X-ray spectroscopy revealed Mn element proportions of 18.32% and 24.82% on chemically modified quartz sand and zeolite surfaces, respectively, primarily existing as MnO₂. Maximum specific surface areas (7.26 m²/g and 28.57 m²/g) and pore volumes (0.0052 cm³/g and 0.112 cm³/g) were attained for chemically modified quartz sand and 300℃-calcined zeolite. The 400℃-calcined zeolite exhibited peak specific surface area (20.18 m²/g) and pore volume (0.0857 cm³/g). Chemically modified zeolite demonstrated the shortest maturation period for iron and manganese removal, requiring only 10 and 8 days respectively, significantly shorter than unmodified materials. This research provides theoretical foundations and technical references for developing iron-manganese removal technologies in groundwater applications for aquaculture.

Aiming at the problems of significant noise interference in sonar images and insufficient representation capability under small-sample conditions in fish feeding behavior recognition, this paper proposes a dual-stream spatio-temporal attention network that fuses domain knowledge and deep features. First, an improved wavelet filtering algorithm is proposed to effectively remove bubble noise in sonar images. Then, a dual-stream feature fusion architecture is designed, where the statistical feature stream includes 6-dimensional features such as target quantity and spacing standard deviation, and the deep feature stream extracts high-order semantic features of sonar images through the Residual Network (ResNet18). Meanwhile, a Long Short-Term Memory network (LSTM) is introduced to capture the temporal dependency of behavior sequences, and a spatio-temporal cross-attention mechanism is combined to adaptively focus on key frames and target areas. Experiments on the self-built dataset show that the classification accuracy of this network reaches 77.0%, among which wavelet denoising, dual-stream fusion, and spatio-temporal attention mechanism contribute precision improvements of 1.8%, 5.9%, and 2.8% respectively, verifying the effectiveness of each component. This study provides a new method for underwater target behavior recognition.

The feeding model and control system of the industrialized South American white shrimp farming system are disconnected, and a closed-loop feeding decision control system has not yet been formed, resulting in insufficient intelligence level of the integrated management and control system for aquaculture production. Therefore, a shrimp feeding dynamic decision-making system based on multi-source information fusion has been developed. The system integrates underwater cameras and dedicated feeding platforms to construct a synchronized monitoring system for shrimp body length and residual bait amount; On this basis, a dual factor feeding decision model that integrates growth status and real-time residual feeding feedback is proposed, endowing the system with adaptive optimization capability; Further design a control execution unit with PLC and upper computer as the core to drive the high-precision feeding machine to achieve reliable implementation of strategies and data management. Comparative experiments have shown that the system significantly improves feeding control accuracy and feed utilization efficiency, increasing shrimp body mass growth rate by 0.26%, reducing feed coefficient by 0.14%, and increasing survival rate by 0.9%. This provides a reliable technical solution for the refinement, intelligence, and sustainable development of shrimp farming.

Significant progress has been made in the artificial restoration of fish habitats, and artificial fish nests are currently one of the main measures for the proliferation of fishery resources and habitat restoration. This article develops a new type of semi submersible environmentally friendly composite artificial fish nest. The entire device adopts a "top bottom" double-layer structure, suspended in water, with a sturdy and durable structure and good environmental performance. By conducting experiments around two bird islands in Baiyangdian, the synergistic mechanism of artificial fish nests on fish resource restoration was systematically analyzed. The results showed that the artificial fish nest increased fish resources by 73.76% to 120.96%, and had a significant aggregation effect on fish with ecological habits of producing and sinking sticky eggs and laying bivalve eggs. After the artificial fish nest was deployed, it could overall increase the unit fishing effort of fish and provide attachments for fish spawning. The results indicate that the artificial fish nest has shelter, foraging, and reproductive functions. This study provides innovative solutions to solve the problems of single function and insufficient ecological sustainability of traditional artificial fish nests, aiming to provide technical reference for the protection and restoration of large surface fisheries.

Underwater biological target detection still predominantly relies on manual identification methods, facing challenges related to low levels of intelligence. Existing target detection algorithms, such as the YOLO series, suffer from issues such as large parameter counts, high computational requirements, and poor detection accuracy. This paper proposes an improved algorithm based on the RT-DETR model. The DynaShareNet backbone network is introduced, which shares stem information architecture to enhance feature fusion efficiency and reduce computational burden; the Dilated Transformer Attention Block (DTAB) is introduced to combine global and local feature interactions to enhance robustness in complex underwater environments; the MaSA-RetBlock module is adopted to address target blurring and low-contrast recognition issues; and the EMASlideVarifocalLoss is introduced to enhance the ability to handle difficult-to-classify targets. Experimental results on the URPC2020 dataset demonstrate that the improved algorithm significantly enhances detection accuracy, with mAP50 and mAP50:95 improving by 3.3% and 3.5%, respectively, while significantly reducing model complexity, with parameter counts and computational costs decreasing by 41.7% and 47.7%, respectively. The detection accuracy and parameter count/computational complexity outperform YOLO series algorithms, and the algorithm demonstrates excellent generalization performance on the RUOD dataset. The study indicates that the improved algorithm effectively enhances the performance and efficiency of underwater target detection, offering promising application prospects.

 Squid industry is an important part of my country's marine economy, but the squid processing industry is limited by manual labor, which seriously restricts the upgrading of the industry. In order to realize the automation of the separation process of squid carcass, viscera, fins and heads, this study designed a carrier with a "fin-clamping and neck-hanging" fixed mode for squid according to the structural characteristics of squid, and designed a squid automatic feeding module, squid yellowing, yellowing, finning and head removal structure by analyzing the characteristics of manual labor. The automatic feeding module realizes squid posture recognition based on the Faster-RCNN neural network model, realizes squid grabbing point solution based on the fast DT algorithm, and uses collaborative arms to complete the automatic feeding of squid; the yellowing, yellowing, finning and head removal structure fusion control program realizes human-like operation. The test results in the squid processing plant show that the fully automatic removal equipment for squid by-products can realize the automatic feeding, visceral removal, finning and head removal of squids in the specification range of 100-400g, with a processing efficiency of 45 pieces/minute, and a single device can save 3 people. Research has shown that the fully automatic squid by-product removal equipment can simulate humans to complete the work of removing squid viscera, fins and heads, achieving the same operating effect, reducing labor intensity while improving work efficiency. Its promotion and application can improve the intelligence level of the squid processing industry.

To systematically review the application of computer vision in the field of aquaculture,this paper provides an in- depth analysis of its current implementations and challenges across various stages of the farming process,while also offering insights into future development trends. The aim is to provide theoretical support and technical references for the intelligent transformation and upgrading of aquaculture. This study focuses on the specific application pathways and performance of visual recognition algorithms-such as convolutional neural networks and the YOLO series in aquaculture. It also elaborates on the advantages and development potential of multi-modal fusion algorithms in integrating visual images,acoustic signals,and water quality monitoring data. Existing research demonstrates that computer vision technologies can significantly enhance the precision management and production efficiency of aquaculture operations. Multi-modal fusion algorithms,in particular,have shown outstanding performance in key tasks such as fish behavior recognition and quantitative analysis of feeding intensity. However,computer vision algorithms still face challenges in practical applications,including poor image quality caused by complex underwater imaging environments and increased recognition difficulty due to diverse fish behavior patterns. Looking ahead,with the optimization of deep learning algorithms,further application of multi -modal fusion technology,and cross - disciplinary integration with technologies such as the Internet of Things and aquaculture robotics,computer vision is expected to provide critical technical support for the efficient, precise, and sustainable development of aquaculture. This will play a significant role in ensuring global aquatic product supply and food security.

With the continuous expansion of aquaculture scale and the improvement of its intelligent level,traditional methods such as manual inspection and water quality sampling struggle to meet the fine management requirements of modern aquaculture due to their intrusive nature and lack of real-time capability. Passive Acoustic Monitoring(PAM) technology can accurately analyze the behavioral characteristics of aquatic organisms without disturbing them. Centered on acoustic signals,this technology has established an analytical framework covering data collection,signal processing,feature extraction,and pattern recognition ,demonstrating strong adaptability in actual aquaculture environments. Studies have shown that PAM technology has obvious advantages in low -light,deep -water,and turbid environments,and has exhibited application potential in feeding monitoring,reproduction identification,water quality early warning,and other aspects. However,the further development of this technology is restricted by issues such as equipment noise interference,lack of cross - species databases, and insufficient algorithm generalization. Future development should focus on advancing noise reduction and enhancement,multimodal fusion, establishing a standardized data system,and strengthening interdisciplinary collaboration to promote industrialization.

Rapid decompression resulting from abrupt depth changes during the ascent of deep - sea aquaculture cages can induce severe stress or even mortality in physoclistous fish,becoming a critical bottleneck in the development of offshore aquaculture. In this study, Larimichthys crocea was used as a model species to investigate behavioral and physiological responses under different decompression rates and staged decompression strategies. A laboratory-based hyperbaric simulation system was constructed to replicate the ascent process from 20 m depth(200 kPa) to the surface. Two decompression modes constant rate and staged decompression were designed, with behavioral responses recorded and quantified using image processing techniques. Key metrics included instantaneous swimming speed,surface probing frequency,and tail beat frequency. Results showed that under constant-rate decompression,fish exhibited the most stable behavior at 10 kPa/min,with an average swimming speed of 0. 028 m/s, probing frequency of 18 times/min, and tail beat frequency of 1. 61 Hz. In the staged decompression group,a 5-minute pause at 50 kPa ( approx. 5 m depth) significantly alleviated stress,with swimming speed decreasing from 0. 085 to 0. 037 m/s,probing frequency from 31. 11 to 1 time/min,and tail beat frequency from 1. 56 to 0. 52 Hz. Over half of the individuals displayed clear behavioral recovery during the pause,a pattern not observed in other treatments.These findings indicate that a decompression strategy combining a moderate rate( 10 kPa/min) with a brief pressure hold at 50 kPa can effectively balance operational efficiency and animal welfare. This study provides a scientific basis for decompression management during cage lifting in the offshore farming of L. crocea and other physoclistous fish.

This study aimed to investigate the growth differences and aquaculture water quality changes of Litopenaeus vannamei in modern industrialized recirculating aquaculture systems under outdoor and indoor conditions. This study set up two models of aquaculture:outdoor pond greenhouse recirculating aquaculture and indoor factory recirculating aquaculture. Under the same initial stocking size,stocking density,and culture duration,the growth patterns and water quality variations of Litopenaeus vannamei were investigated. The results showed that Litopenaeus vannamei exhibited rapid growth in both modes,with specific growth rates(SGR) of body mass exceeding 1. 62%. The outdoor mode demonstrated faster growth,achieving an SGR of 2. 05%.The harvest sizes were(9. 50± 0. 81) cm and( 11. 03± 2. 60) g for the indoors mode,and( 10. 17± 0. 76) cm and( 12. 98± 2. 27) g for the outdoors mode,with survival rates exceeding 80. 9% in both modes. A strong power-function relationship(W= aLb ) was observed between body length and mass in both modes,with equations of W= 0. 009 6 L3. 075 8 (R2 = 0. 915,P<0. 01) for the outdoor mode and W= 0. 014 L2. 915 3 (R2 = 0. 860 4,P<0. 01) for the indoor mode. The b-values were closed to 3, indicating isometric growth between body length and mass. Additionally, quadratic relationships were found between body length ,body mass,and culture time(Outdoor breeding mode L= -0. 000 8 t2 +0. 080 7 t+8. 478,R2 = 0. 980 7,P<0. 01;Indoor breeding mode L= 0. 001 t2 +5× 10-5 t+8. 517 3,R2 = 0. 957 6,P<0. 01; Outdoor breeding mode W= -0. 007 1 t2 +0. 439 9 t+ 6. 056 3,R2 = 0. 909, P<0. 01; Indoor breeding mode W= -0. 000 7 t2 +0. 173 8 t+6. 149,R2 = 0. 997 3,P<0. 01). Significant differences were observed in water quality indicators(TAN,NO -N,NO -N,CODMn ,TN,and TP) between the two modes. The outdoor mode exhibited significantly lower TN and TP concentrations compared to the indoor mode,along with lower NO -N concentrations in the late culture stage. However, NO - N concentrations were higher in the early to mid - culture stages outdoors. No significant regular patterns were observed for TAN and CODMn concentrations,although both decreased in the late culture stage. Research indicated that Litopenaeus vannamei grows faster and the water quality was better in outdoor pond greenhouse recirculating aquaculture compared indoor factory-based recirculating aquaculture systems,making it suitable for breeding promotion. The research provided a paradigm for the exploration of modern facility -based breeding patterns and intelligent management of Litopenaeus vannamei.