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.

This study addresses the challenge of efficiently removing bottom solid waste in intensive aquaculture tanks by focusing on rectangular aquaculture tanks. A two-dimensional transient model, coupling the standard k–ε turbulence model with the VOF free surface model, was developed to optimize a mechanical scraper discharge system. Using an L25(5³) orthogonal design, the effects of scraper height (6–14 cm), tilt angle (−20° to +20°), and speed (0.05–0.09 m/s) on particle distribution and discharge efficiency were analyzed. The results showed that the tilt angle had the most significant impact, with the optimal parameters being 6 cm height, +10° tilt, and 0.05 m/s speed, achieving the highest efficiency and minimal residue. To validate the numerical model, 45 single-factor tests were conducted on a physical platform using particle recovery to quantify removal efficiency. Results deviated by only 5%–10% from simulations, confirming model accuracy. The study supports the engineering optimization of mechanical scraper systems.

To address the challenges of low detection accuracy and high false positive rates caused by the complex morphology, significant scale variations, and blurred boundaries of lesion areas in Carassius auratus diseases, this paper proposes a novel recognition model named CAI-YOLO based on the YOLOv11 framework. First, the backbone network incorporates the ConvNeXt V2 module. This module utilizes a self-supervised pre-training strategy based on Masked Auto Encoders and introduces a Global Response Normalization layer, effectively mitigating feature collapse and enhancing feature diversity. Second, the neck network integrates AKConv, which leverages an adaptive sampling mechanism to improve the model's multi-scale modeling capability for irregular disease spots. Finally, the loss function employs IF-IOU, which combines the internal constraints of Inner-IOU with the re-weighting mechanism of Focaler-IOU, thereby accelerating model convergence and improving localization accuracy. Experiments conducted on a self-built Carassius auratus disease dataset show that the CAI-YOLO model achieves Precision, Recall, mAP@0.5, and mAP@0.5:0.95 of 85.6%, 87.8%, 86.7%, and 58.6%, respectively. Compared to the baseline YOLOv11n, the mAP@0.5 and mAP@0.5:0.95 are increased by 0.9 and 1.1 percentage points, respectively. Furthermore, the number of parameters, computational complexity, and model size are reduced by 10.89%, 8.19%, and 7.84%, respectively. The research demonstrates that the CAI-YOLO model effectively enhances overall detection performance while simultaneously reducing computational resource requirements, providing a valuable reference for the lightweight design and practical application of Carassius auratus disease detection systems.

Biofloc Technology (BFT), an environmentally sustainable aquaculture approach, harnesses in situ microbial processes to purify water, supply supplemental nutrition, and suppress pathogens, thereby serving as a cornerstone for advancing sustainable aquaculture. This review systematically addresses core technical challenges inherent in BFT applications, with particular emphasis on constraints stemming from species-specific physiological requirements, the diversity and economic feasibility of carbon sources, dynamic fluctuations in critical water quality parameters, and overall system stability. Our analysis underscores that precise control of the carbon-to-nitrogen (C/N) ratio, judicious selection of carbon sources, efficient dissolved oxygen (DO) management, continuous alkalinity replenishment, and stable regulation of suspended solids are fundamental to maintaining system functionality. To overcome these challenges, we integrate key strategies—including optimized carbon supplementation protocols, refined microbial community management, innovative aeration system design, and intelligent real-time monitoring technologies. Finally, we delineate future development pathways for BFT, focusing on standardization, intelligent automation, multi-trophic integration, and enhanced energy efficiency, thereby offering theoretical foundations to accelerate its adoption at industrial scale.

In response to the actual needs of Sanya Bay Marine Ranch, this paper designs a porous box-shaped artificial reef with an internal shelter tube and the function of juvenile fish care. Based on the structural and flow field characteristics, this porous box-shaped artificial reef was compared and analyzed with three traditional reef structures. In addition, the flow field characteristics of the porous box-shaped artificial reef under different inflow velocities, flux-facing angles, transverse and longitudinal layout spacings were explored by combining numerical simulation. The research results show that the hydrodynamic characteristics of the four reef structures are significantly different. Among them, the volume of the upwelling flow and the back nest flow of the porous box-type artificial reef is the largest, followed by the square A-type artificial reef and the hollowed-out frame-type artificial reef, and the square B-type artificial reef is the smallest. Moreover, the stability of the four reef structures all meets the requirements, and the porous box-shaped artificial reef has prominent advantages in terms of surface area and flow field effect. The flow field parameters of the porous box-type artificial fish reefs do not change significantly with the increase of the incoming flow velocity. The flow field parameters of the artificial fish reefs show a trend of first decreasing and then increasing with the increase of the flow angle. When the flow angle is 45°, the upflow volume, the length of the back-burrow flow, and the volume are the largest. It is recommended to use this angle for deployment. The lateral spacing is negatively correlated with the flow field parameters of the artificial fish reef group. It is recommended to use a spacing of 0.5 times the reef length for layout, which can effectively suppress cross-flow and enhance cooperative blocking. The longitudinal spacing is positively correlated with the flow field parameters of the artificial fish reef group. It is recommended to use a spacing of 2.0 times the reef length for layout, in order to promote the superposition and coupling of the flow fields of adjacent reefs and expand the slow-flow area.

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.

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.

At present, the processing of shells for laver seedling cultivation mainly relies on manual edge milling and drilling, which is inefficient and poses safety risks. Therefore, this study designed an automatic shell milling and drilling device for laver seedling cultivation integrated with a PLC control system. The working principle of the device is explained, and structural design and strength verification of the core components of the device are carried out based on theoretical calculation and finite element analysis methods.By building a prototype, the shell breakage rate was used as the test index, and a Box-Behnken experimental design method was adopted to conduct significance tests on the drilling speed, drill feed rate, milling cutter speed, and shell self-rotation speed. The test results were analyzed and optimized to determine the value range of each factor. The test results show that the device operates stably as a whole. Under the optimized experimental conditions of a drilling speed of 3300 r/min, a feed rate of 0.65 mm/s, a milling cutter speed of 4800 r/min, and a self-rotation speed of 11 r/min, the shell breakage rate is only 1.75%, meeting the requirements for productization of shell drilling and milling processing.Research shows that this automatic drilling and milling device has good machining performance, can significantly reduce the breakage rate in the processing of shells used for purple laver seedling cultivation, and has a good application prospect.

To address the problems of incomplete cleaning of oyster shells and low efficiency in traditional oyster cleaning machines, this paper studies the ultrasonic cooperative cleaning technology and develops an ultrasonic cooperative cleaning device for oysters. Based on the COMSOL Multiphysics finite element simulation technology, the distribution of the ultrasonic field was simulated, and the influence laws and pressure distribution of the ultrasonic cleaning frequency, cleaning cross-section height, and the number of transducers on the sound pressure and sound pressure level in the ultrasonic cleaning domain were clarified. The research shows that the optimal energy distribution of the ultrasonic field is achieved when the ultrasonic cleaning frequency is in the range of 25-45 kHz, the cleaning cross-section height is -125 mm, and the number of transducers is 10. The sound pressure value range is 2.5-4.8×104 Pa. A prototype was manufactured and the cleaning performance was verified. By exploring the effects of feeding rate, cleaning time, and ultrasonic cleaning frequency on the overall cleaning performance of the machine, the results show that the order of influence of each factor on the oyster impurity removal rate is feeding rate > ultrasonic cleaning frequency > cleaning time. Under the operating conditions of an ultrasonic cleaning frequency of 28 kHz, a cleaning time of 5 minutes, and an oyster feeding rate of 1150 g, the impurity removal rate of the device is 5.63%. This study can provide a reference for the development of ultrasonic cleaning devices for oysters.

Antarctic krill (Euphausia superba) is an important strategic marine resource globally, and improving its fishing technology is essential for increasing catches for each national fishing industry. Currently, the Antarctic krill fishery is entering a new phase of resource competition driven by continuous technological developments in fishing. Although China has successively deployed several professional continuous fishing vessels, it still faces challenges such as reliance on imported core equipment and low fishing efficiency. This paper systematically reviews the development and application of Antarctic krill fishing technologies, with a focus on continuous trawling methods and the existing challenges in areas such as precise krill swarm detection, fishing path planning, and dynamic fishing depth adjustment. We propose the establishment of an intelligent continuous fishing technology system, which is centered on precise krill swarm detection, intelligent decision-making for fishing paths, and coordinated control of operational fishing gear layers. The overarching objective is to promote the high-quality development of China's Antarctic krill fishery towards intelligent precision and enhance the competitiveness of catch shares.

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.

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.

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.   

To explore the responses of Sebastes schlegelii to different dissolved oxygen environments,We selected juvenile S. schlegelii with an average weight of (0. 520 ± 0. 130 g) and set up 4 groups of different DO ( 3,5,7, 10 mg/L). The experimental period was 28 days, and the effects of different dissolved oxygen levels on the movement behavior, growth performance,and blood biochemistry of juvenile S. schlegelii were observed and analyzed. The results showed that : with the increase of dissolved oxygen levels,the spontaneous swimming speed,inter-individual distance and nearest neighbor distance of the experimental fish significantly increased and reached the highest in group 10 mg/L,while cohesion,coordination and polarity gradually decreased. With the increase of dissolved oxygen levels,the survival rate,specific growth rate,feed conversion rate ,liver-to-body ratio and viscera-to-body ratio of the experimental fish significantly increased and reached the maximum in Group 10 mg/L. With the increase of dissolved oxygen levels,the contents of total protein,total cholesterol and triglyceride in the blood of the fish gradually increased,the contents of glucose and alanine aminotransferase first decreased and then remained stable,the content of aspartate aminotransferase first decreased and then increased.

To optimize acoustic conditioning parameters for enhancing the aggregation effect and stock enhancement efficiency of Sebastes schlegelii, this study applied continuous pulsed sounds at 200 Hz, 300 Hz, and 500 Hz over a 20-day acoustic conditoning period. The effects on shoaling behavior (response time, aggregation time, aggregation rate) and physiological indicators (plasma cortisol, total protein, alanine aminotransferase) were evaluated. Results showed that the 200 Hz group exhibited the fastest behavioral response during the initial stage (days 1~4), while the 300 Hz group achieved the highest aggregation rate in the mid-stage (days 2~7). In the later experimental stage (days 8~20), all acoustic conditioning groups demonstrated significantly better shoaling behavior than the control group (P < 0.05). The final aggregation rate was highest in the 300 Hz group (73.74%), followed by the 200 Hz group (69.1%) and the 500 Hz group (63.51%). Physiologically, levels of alanine aminotransferase (ALT) and cortisol (COR) increased transiently before recovery. The 300 Hz group showed the smallest ALT peak (30.79 ± 1.10 pg/mL), and its COR levels returned to baseline faster than those in the 200 Hz and 500 Hz groups. Total protein (TP) levels showed only minor overall fluctuations, indicating the mildest stress fluctuation in the 300 Hz group. In conclusion, the 300 Hz acoustic frequency effectively promotes shoaling behavior in Sebastes schlegelii while inducing less physiological stress compared to 200 Hz and 500 Hz, making it the preferred parameter for acoustic technology optimization. This finding holds practical significance for improving resource management efficiency in marine ranching.

To address challenges in underwater fish detection caused by turbid water and uneven illumination—such as blurred edges,high missed-detection rates,and the high computational cost of existing models,this paper proposes CRL-YOLO11,an improved lightweight detection algorithm based on YOLOv11n. Firstly,an Edge -Aware and Context -Guided Attention Block is proposed to enhance the model ’s perception of weakly represented targets,thereby improving the detection of small fish targets. Secondly,a lightweight and efficient aggregation module is designed,which leverages a re-parameterized multi-branch structure to enable cross-scale feature fusion and reduce information loss during feature propagation. In addition,to address the issue of high computational cost,a selective channel down-sampling module and a lightweight asymmetric detection head are introduced. Results obtained on the self-built dataset demonstrate that, compared with the baseline model YOLOv11n,the proposed CRL-YOLO11 achieves a 2. 1% improvement in mAP50 and a 2. 9% increase in recall,while reducing the model's weights and parameters to 86% and 82% of the original,respectively. Furthermore,on the Kaggle-Fish dataset,the mAP50 increased by 0. 7% and recall by 2. 4%. On the URPC2019 dataset,the mAP50 improved by 1. 1%. The experimental results demonstrate that the proposed model offers a balanced trade-off between detection accuracy,generalization,and deployment efficiency,rendering it well - suited for real-time underwater object detection in smart fisheries and aquaculture environments.

Aiming at the problems existing in current fish barrier electric fences on the market, such as non‑continuously adjustable output pulse voltage, easy corrosion of unipolar pulse electrodes, and generally low system scalability and networking level, a fish barrier electric fence device is designed based on power electronics, automation, and Internet of Things (IoT) technologies.An Omron network‑type PLC is used as the central control core, combined with IoT gateway communication, to achieve easy system expansion, networked control, and remote operation. A PWM pulse generator outputs high‑frequency (50 kHz) pulses to control the on‑off states of the MOSFET in a synchronous BUCK circuit, thereby realizing continuous voltage regulation. The same PWM pulse generator outputs low‑frequency (≤30 Hz) pulses to control the on-off states of the IGBT in the pulse generating circuit, achieving power supply pulses with adjustable frequency and duty cycle. Specialized driver chips and protection circuits are selected and designed to ensure safe and reliable on‑off control of the MOSFET and IGBT.The results show that the continuously adjustable pulse voltage output by the fish barrier electric fence provides better adaptability, enabling the fish barrier effect to match the environment. The effective voltage of the bipolar pulse fish barrier electric fence is 10~15 V lower than that of the unipolar pulse type, and the effect is more significant. This study provides partial experimental basis and data for research on electric fence fish barriers and also offers a technical design reference for the development of fish barrier electric fence system equipment.

In order to investigate the effects of different stocking densities on the growth performance, antioxidant capacity and immune function of Siniperca chuatsi in a factory farming production system, and to preliminarily determine its suitable stocking density. Three stocking density groups of low (M1, 150 fish/m³), medium (M2, 200 fish/m³) and high (M3, 250 fish/m³) were set up to carry out the 180d culture experiment. The growth indexes of Siniperca chuatsi were measured and the expression levels of antioxidant enzyme activities and related immune factors in the head, kidney and spleen were analysed. The results showed that at the end of the culture period (180 d), the M2 group was significantly better than the M1 and M3 groups in terminal body mass, body length and weight gain rate (P < 0.05). The antioxidant capacity tended to decrease with increasing density, with a significant decrease in superoxide dismutase (SOD) activity and total antioxidant capacity (T-AOC) content, and a significant increase in malondialdehyde (MDA) content; the activities of non-specific immunoenzymes (AKP, ACP, LYS) and the activities of pro-inflammatory factors (TNF-α, IL-8), interferon pathway-associated molecules (IRF11, IFP35) and T-cell receptor (TCRα) content were significantly upregulated with increasing density. It was shown that excessive high density induces oxidative stress and chronic inflammation, although it can stimulate the immune response. The present study suggests that the density of Siniperca chuatsi in factory culture should be around 28.61 kg/m³ in order to achieve a balance between culture efficiency and fish health.

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.

A detection model LSD-YOLO based on improved YOLOv11n is proposed is proposed to address the problem of underwater aquaculture fish due to occlusion, image degradation, and difficulty in realizing accurate tracking of the fish. Firstly, a DynamicHead is introduced to give the model the ability to fuse task awareness, scale awareness, and spatial awareness. Second, a lightweight feature extraction module, LiteODSE, has been designed to combine dynamic convolution and channel attention to enhance the feature extraction capability in the backbone network. Then, the SDI multilevel feature fusion module is introduced, which can separate and fuse multi-scale spatial information. Moreover, the GIOU loss function is used instead of the CIOU loss function, and the difficult localization problem under small targets as well as non-overlapping regions can be improved by introducing the constraint information outside the bounding box. Finally, tracking accuracy is effectively improved by combining it with StrongSORT, which is currently a more advanced tracking algorithm. Experiments demonstrate that the accuracy of the designed model is improved by 3.2% and mAP50 by 3% compared with YOLOv11n. Compared with YOLOv11n+StrongSORT, the MOTA is improved by 5.2% and the number of ID switching is reduced by 30%, which proves that the improved method can be better applied in target detection and tracking of underwater farmed fish.

As a major country in the cultivation of burying shellfish such as clams and razor clams,China's technology and equipment level in the harvesting process are directly related to the sustainable development of the industry. To enhance the sustainable development of the burying shellfish aquaculture,this study systematically reviews the mainstream shallow sea burying shellfish harvesting equipment(drag rake type,hydraulic type,rotary tooth type,propeller type,and vibration type) at home and abroad,analyzes their working principles,structural characteristics,and harvesting effects. By comparing the current research status of shallow sea shellfish harvesting equipment at home and abroad,this study reveals the bottlenecks of weak theoretical mechanism research,lack of ecological impact assessment,insufficient specialized design,and lagging intelligent control in domestic equipment. Propose a targeted independent research and development path:deepen the theory of fluid solid coupling in the harvesting process,and develop harvesting equipment suitable for China  s shallow sea bottom-sown mode ;Building an ecological benefit oriented“ harvesting restoration”collaborative system; Promote the integration of mechanization and intelligence technology,achieve precise control and visualization of harvesting trajectories,and provide technical support for breaking through industrial constraints and promoting green aquaculture.

To overcome the inefficiency, high expense, and subjective inconsistency of manual grading, we introduce a lightweight oyster shape recognition approach built upon an enhanced YOLOv10 framework. . First,the backbone of YOLOv10n is replaced with PP -LCNet; second,a Focused Linear Attention module is inserted into the PSA blocks of the backbone; third,the upsampling operator in the neck is substituted with the DySample dynamic upsampler; ultimately, the original activation is superseded by the AReLU function. Compared with the original YOLOv10n,the enhanced model trims floating- point operations by 20. 7% ,parameters by 22. 2% ,and overall size by 22. 4% ,while raising precision to 94. 4%—a 3. 8-point improvement. The proposed approach not only provides an effective solution for oyster shape recognition but also offers technical support for developing automatic identification and classification systems,thereby advancing the intelligent development of oyster sorting technology.

To address the issue of feed breakage during hydraulic conveying in aquaculture that affects feeding efficiency and water environment. This study employs a CFD-DEM coupled method to develop a solid-liquid two-phase flow model for particle breakage. The research thoroughly investigates the breakage behavior of cylindrical feed particles within curved pipes during hydraulic conveying, with a particular focus on breakage types, fragment size distribution, and mechanisms of energy transformation and dissipation. The results indicate that feed particle breakage primarily manifests as surface peeling, occurring progressively under shear stress rather than via complete fracture. The size distribution of breakage products exhibits a bimodal pattern: large fragments retain the size of the parent particles, whereas small fragments are numerous but contribute relatively little to total mass. Energy dissipation is especially pronounced during collision-induced breakage, with up to 45.2% of energy lost during the initial breakage event, mainly due to the conversion of translational kinetic energy into internal and rotational kinetic energy. This study systematically reveals the breakage behavior of feed particles during hydraulic conveying from three perspectives—microscopic mechanisms, characteristics of breakage products, and energy transformation—providing a theoretical basis for optimizing hydraulic conveying processes to reduce particle breakage.

In order to address the challenges of insufficient ambient lighting, small target size, and target clustering and occlusion leading to decreased detection accuracy in underwater target detection, this study proposes a multi-scale underwater target detection algorithm, FDM-YOLO, based on receptive field features. First, to address the issues of insufficient underwater ambient lighting and the fact that underwater organisms are often small targets with colors similar to their surroundings, the RFCADown module is used to generate large receptive field spatial features, enhancing the extraction of key information about underwater targets. Second, a Dysample upsampling module is introduced to suppress blurring and distortion in traditional upsampling processes. Third, a multi-scale, multi-dimensional information collaboration module, C3K2-IMCA, is designed to improve the representation performance of densely occluded targets. Finally, WIoU is used instead of CIoU loss function to mitigate the negative impact of extreme-shaped bounding boxes on model training for small targets. Experimental results show that FDM-YOLO achieves a 2.1% and 2.0% improvement in mAP50 and mAP@50-95 respectively compared to the benchmark model on the DUO dataset, while the model parameters and computational cost are only 2.35M and 6.0 GFLOPs. The above results verify the efficiency of the improved model in enhancing the detection performance of small underwater targets.

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.

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.

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. 

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

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.

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.

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.

Squid is a marine mollusk, stands out as one of the most commercially valuable seafood resources globally, with profound economic significance for China’s marine fisheries sector. As a key species in China’s aquatic product supply chain, squid contributes significantly to the national marine catch volume: data from 2024 shows that China’s total squid catch reached 317,325 tons, accounting for 32.97% of the country’s overall marine catch. This substantial output underscores the urgent need for efficient and precise processing technologies to maximize the economic value of squid products, particularly in the segment of squid tentacle slicing, an essential step in producing value-added products such as frozen squid slices, canned squid, and ready-to-eat seafood snacks. Against this backdrop, this project proposes a novel quantitative cutting solution for squid tentacles, integrating line laser scanning, 3D point cloud reconstruction, improved deep learning, and optimized algorithmic decision-making. The implementation process consists of three core stages: First, a line laser scanning platform was constructed to capture the 3D morphological information of squid tentacles. Given that a single-angle laser scan can only obtain partial point cloud data, the platform performs multiple laser scans of the same squid tentacle from different angles. The acquired multi-angle incomplete point cloud datasets are then processed through point cloud matching and surface reconstruction. This process ultimately synthesizes a complete, high-resolution 3D point cloud model that accurately represents the entire morphological structure of the squid tentacle, including details such as suction cup distribution and local diameter variations. Second, an improved Generative Adversarial Network deep learning model was established to address the potential inefficiency of multi-angle scanning in industrial scenarios. The key improvement lies in integrating an attention mechanism into both the autoencoder and decoder modules of the original GAN architecture. This attention mechanism enables the model to dynamically weight and emphasize valuable feature information during the learning process, while downplaying irrelevant or noisy data. The trained model can efficiently reconstruct the complete 3D structure of a squid tentacle from a single incomplete point cloud, significantly reducing scanning time while maintaining morphological accuracy. Comparative experiments show that the improved GAN model outperforms the baseline GAN model by 6.2% in the Intersection over Union (IoU) index and by 42.4% in the Cross-Entropy (CE) index, demonstrating its superior performance in 3D structure reconstruction. Finally, the quantitative cutting of squid tentacles was transformed into a multi-objective optimization problem, with the core objectives being: minimizing the weight error of each sliced piece and maximizing the overall utilization rate of the squid tentacle. To solve this problem, the Simulated Annealing  algorithm was improved by incorporating domain-specific constraints from squid processing. Cutting tests were then conducted on the 3D point cloud models of squid tentacles using the improved SA algorithm. Experimental results confirm that under the constraint of a single-slice weight error≤8%, the average utilization rate of squid tentacles reaches 87.3%, a significant improvement of 27.3–37.3 percentage points compared to the 50–60% utilization rate of manual slicing. In summary, this project develops a comprehensive quantitative cutting technology for squid tentacles that integrates 3D sensing, intelligent reconstruction, and optimized decision-making. It effectively addresses the inefficiencies, low precision, and high waste of traditional manual and mechanical cutting methods, providing a feasible technical solution for the industrial upgrading of the squid processing industry and laying a foundation for the intelligent transformation of aquatic product processing.

 In view of the characteristics of high salinity of suspended particles in the tail water discharged from high-density intensive aquaculture, and the low efficiency and high cost of traditional tail water treatment equipment, this study designed a centrifugal desalting and slag collection machine integrating centrifugal dehydration, high-pressure spraying and negative-pressure collection, completed the structural design and control system of the equipment, and made a prototype to carry out performance tests. The raw water mixed with feed and seawater is used to simulate the actual tail water, and the collection rate, desalination rate and water content of treated solid particles of the equipment are taken as indicators. By using the single factor experimental design method, the performance test is carried out under the working conditions of the same equipment operating parameters and different liquid-solid concentration ratios, and the treated solid particles are dried to calculate the index value of the equipment. The results show that the equipment has a good effect of solid-liquid separation and salt reduction. The collection rate of solid particles in aquaculture tail water can reach over 65%, the desalination rate is over 80%, and the water content of treated solid particles is about 65%. The research shows that the equipment can significantly reduce the pollution risk of water and soil caused by direct discharge of tail water, and realize the resource utilization of tail water tailings, which has a good prospect of popularization and application.

To enhance the cage’s resistance to extreme sea conditions,a ballast water system was designed in compliance with regulatory requirements,and buoyancy tests were conducted at the farming site. During the trials,two methods -inclination sensor and liquid level telemetry conversion-were employed to monitor the cage ’s inclination. The results demonstrated that : The cage’s buoyancy process lasted approximately 6 hours, meeting design specifications; Buoyancy speed exhibited a negative correlation with the cage’s cross-sectional area,with pronounced velocity fluctuations observed at sectional transition points; The maximum measured horizontal/longitudinal angles reached -2. 5° and 1. 25° respectively,while calculated values peaked at -1° and 1° ; The inclination data from both measurement methods showed consistent trends throughout the test. The ballast water system's performance was confirmed via buoyancy testing,where the inclinometer's enhanced sensitivity facilitated real-time net cage tilt detection. The dual-monitoring approach provided high-precision data for the optimization design of deep-sea cages.

To achieve precise detection of underwater biological targets and support the sustainable development of marine resources ,this paper proposes an improved YOLO11n-based detection algorithm. Building upon the YOLO11n baseline model, the algorithm introduces the PPA ( Parallel Patch Attention ) module to enhance feature extraction capability for small underwater targets; employs the Detect_ Efficient module to optimize the detection head and improve multi - scale target detection accuracy; and incorporates the CSFCN feature calibration module to address feature loss caused by the lack of global contextual information during convolution,thereby boosting detection accuracy in blurred underwater images. Compared with the original YOLO11n,the improved model achieves a 1. 9% increase in mAP @ 0. 5 for underwater target detection. When compared to mainstream object detection algorithms, the proposed model also demonstrates superior performance in both precision and recall,reaching 85. 6% and 75. 5% ,respectively. Experiments verify that the improved YOLO11n exhibits better detection performance in underwater target detection tasks compared to mainstream models.

 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.

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.

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.