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.

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.

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.

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.

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.

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.

This study aims to construct a mixed culture system of Oocystis and diation Phaeodactylum tricornutum,and evaluate its potential for removing nitrogen and phosphorus as well as treating antibiotic-containing aquaculture tailwater. The nitrogen and phosphorus removal capacity was evaluated by monitoring water quality parameters(NH -N,NO -N,NO -N,PO- -P ,and CDO) ,assessing carbon capture capacity by monitoring microalgae growth,and exploring the stress response of microalgae to antibiotics by monitoring the chlorophyll a content and antioxidant enzyme activity. Results showed that the mixed algae system achieved a 100% removal rate of ammonia nitrogen. The concentrations after treatment of nitrate nitrogen, nitrite nitrogen,total nitrogen,total phosphorus,and chemical oxygen demand were 13. 50± 1. 17 mg/L,0. 20± 0. 01 mg/L,15. 09 ± 1. 14 mg/L,0. 71 ± 0. 03 mg/L,and 31. 57 ± 2. 51 mg/L,respectively,with corresponding removal efficiencies of 86. 03% , 67. 33% ,86. 13% ,94. 87% ,and 50. 60%. The concentration of CIP was 4. 52±0. 45 mg/L,with a removal efficiency of 90. 95%.The carbon capture rate(CO2 -equivalent) of Oocystis reached its peak with 0. 47 g/(L ·d) during the logarithmic growth phase. Microalgae enhanced their antioxidant capacity by increasing the activities of CAT and POD. This study indicates that the mixed algae culture system has a positive effect on nitrogen and phosphorus removal,as well as ciprofloxacin removal. It provides new perspectives for the resource utilization of aquaculture tailwater and important practical application.

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.

In the photovoltaic pond environment,the positioning of traditional unmanned ships relies on satellite systems,which are easily blocked by photovoltaic panels, resulting in a significant decline in positioning accuracy. However, the visual synchronous positioning and mapping technology based on point features also has problems such as trajectory drift and error accumulation. Therefore,this paper proposes a binocular vision inertial SLAM system that integrates point - line structural features based on the photovoltaic pond environment,and studies the extraction of centerline features and structural features of the photovoltaic pond. To cope with water surface reflection and illumination changes and improve the quality of line features, this paper improves the ELSED line feature extraction method. To adapt to the environment of photovoltaic ponds,this paper designs elliptic-spherical raster clustering for vanishing points and uses RANSAC to extract structural features on the elliptic- spherical surface. Experimental verification was conducted using the open - source USVInland dataset and the private photovoltaic pond dataset. By comparing the SLAM system in this paper with the ORB - SLAM3 before improvement,the experimental trajectory error decreased significantly by 60% ,and the system tracking frame rate reached 34 frames per second, ensuring the real - time performance of the system. The positioning accuracy met the requirements of unmanned boats in photovoltaic ponds.

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 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.