Abstract:Recirculating Aquaculture Systems (RAS), as an advanced aquaculture technology, achieve efficient utilization of water resources and a significant reduction in feed consumption through meticulous system management and the application of cutting-edge engineering techniques. Despite these advancements, the accumulation of metabolic waste generated during the cultivation process may trigger severe odor issues, posing a potential threat to the stability of the cultivation environment and production efficiency. This review comprehensively examines the mechanisms of odor generation in RAS systems and provides an in-depth analysis of the origins and influencing factors of two key odor-causing substances-geosmin (GSM) and 2-methylisoborneol (2-MIB). Furthermore, this paper systematically reviews the latest research progress in odor removal technologies within RAS systems, covering a variety of methods including biological, physical, and chemical treatments. By thoroughly comparing the advantages and disadvantages of these technologies in terms of efficiency, cost, ease of operation and environmental friendliness, this article aims to provide a theoretical basis and practical guidance for the further optimization and application of RAS technology, with the expectation of promoting the sustainable development of recirculating water aquaculture technology.

Abstract:The growing global demand for seafood underscores a pressing need for efficiency, quality enhancement, and sustainable practices in the fish processing industry. Intelligent fish processing technology, which leverages advancements in artificial intelligence (AI), machine learning (ML), robotics, and big data analytics, is transforming conventional fish processing methods. This review comprehensively analyzes the fundamental technologies underpinning intelligent fish processing, such as machine vision, image analysis, robotic automation, deep learning, and big data analytics. These technologies are instrumental in optimizing processing efficiency, minimizing labor costs, and maintaining high standards of product quality and consistency. Practical applications of these innovations are discussed, including automated sorting and grading, precision slaughtering and cutting, and the use of intelligent packaging systems, which collectively drive improved production efficiency and enhance product competitiveness in the marketplace. Additionally, this paper explores future industry trends, emphasizing the role of the Internet of Things (IoT), cloud computing, and personalized consumer preferences in advancing the sector towards more intelligent and automated processes. Furthermore, the review addresses key challenges in implementing these technologies, such as the need for scalable infrastructure and enhanced data management for real-time monitoring and quality control. By providing a cohesive overview of intelligent fish processing, this paper offers valuable insights that support both scientific exploration and industry adaptation, marking a significant step towards a high-quality, sustainable, and consumer-responsive fish processing industry.

Abstract:Larimichthys crocea is an important commercial fish in China, with an annual production of more than 250,000 tons in recent years. L. crocea is extremely rich in genetic resources, which consisted of wild populations distributed in natural sea areas and breeding lines obtained through decades of selection breeding. There is an urgent need to develop an accurate genetic identification method to distinguish different germplasm of L. crocea to efficiently protect, manage and utilize L. crocea genetic resources. However, the lack of high-throughput genotyping tools for L. crocea and the lack of representative samples of geographical populations have made accurate identifiing genetic resources difficult. Based on the previously developed 55K liquid SNP array ("Ningxin III") for L. crocea, the present study aims to carry out genetic identification for 21 L. crocea populations, including wild populations in coastal China, cultured populations in Fujian and Zhejiang, and multiple breeding lines. The results of population genetic analysis revealed that the L. crocea population could be divided into Nanhai, Mindong and Daiqu populations, among which the genetic differentiation of the Nanhai population was the most significant. The classification results of large yellow croaker populations based on machine learning methods showed that the identification accuracy rate of the geographical group to which unknown L. crocea individuals belong was more than 99%. The breeding lines to which unknown L. crocea individuals belong also had a very high identification accuracy rate. For example, after three generations of genetic selection, a new strain (GS3F3) that had strong resistance against Cryptocaryon irritans had an identification accuracy rate of 99% based on the neural network method. The present research showed that "Ningxin III" chip and machine learning methods could be used to implement quick and accurate genetic identification for L. crocea. The present study provided an effective tool for accurately identifying and managing the genetic resources of L. crocea, intellectual property protection for breeding materials and lines. Also, it provided a reference for the genetic identification of other aquatic organisms. In the future, it is necessary to establish a complete database covering all L. crocea germplasm resources and genetic identification standards, and develop a supporting visual computer program to perform identification work.

Abstract:Growth and survival tests were conducted on 74 families, comprising both selection and introduced populations with fast growth rates to identify single nucleotide polymorphisms (SNPs) and functional genes associated with growth traits in Litopenaeus vannamei. High-throughput genotyping of individuals from 39 families was carried out using the "Yellow Sea Chip No.1" liquid chip, which included 55K SNPs. Genomic genetic parameters for harvest body weight were estimated using the ssGBLUP method. Additionally, the ssGWAS and FarmCPU methods were employed for the first time to discover potentially associated SNPs and candidate genes. The estimation of genetic parameters revealed a heritability of 0.47 for harvest body weight, indicating a moderate to high potential for genetic improvement within the population. The ssGWAS and FarmCPU analyses identified two and six genome-wide significant SNPs (P<1.26×10^-6), respectively, explaining phenotypic variation ranging from 2.71% to 6.76%. A common SNP was also identified among them. Furthermore, both methods separately screened nine and 14 suggestive genome-wide SNPs (P<2.523×10^-5), explaining phenotypic variation ranging from 1.33% to 6.76%. QQ plots demonstrated that FarmCPU exhibited superior control over false positives compared to ssGWAS. Gene annotation of the seven significantly associated SNPs revealed 15 candidate genes related to functions such as the nervous system, muscle development, and energy metabolism. Notably, the common significantly associated SNP was annotated to two genes: LOC113809777, which encoded a hydrolase promoting glycogen hydrolysis, and LOC113809778, a transcriptional regulatory factor associated with gene expression regulation. In conclusion, utilizing the "Yellow Sea Chip No.1" liquid chip, the ssGWAS and FarmCPU methods facilitated the identification of significantly associated SNPs with harvest weight in L. vannamei, offering valuable insights into the genetic basis of growth traits.

Abstract:Fish are an important source of dietary protein for humans, contributing significantly to national food security and public health. The body size of fish holds significant guiding and practical value for both aquaculture and marine fishing industries. With the advancement of computer vision, non-contact measurement methods are gradually replacing traditional labor-intensive manual measurements to acquire fish morphological characteristics. However, current computer vision methods cannot construct complete three-dimensional models of fish, failing to meet the current demand in the aquaculture sector for three-dimensional digital models of fish. this study utilized structured light projection combined with binocular stereovision methods to reconstruct three-dimensional digital models of fish. A fish point cloud acquisition system was designed, incorporating deep learning networks for fish body image segmentation during the acquisition process. Phase-shifting was used to mark the grayscale on the surface of fish bodies, and finally, a binocular stereovision system was employed to reconstruct fish point clouds. The accuracy of the system was validated using the economically important mackerel species (Scomberomorus niphonius). Results indicated that this method could construct fish point cloud models, with relative errors for fork length, body height, maximum body circumference, and posterior gill cover circumference of the reconstructed mackerel models being 0.82%, 4.47%, 3.14% and 2.87%, respectively. Correlation analysis and fitting of the relationships between body measurements showed that the multivariate linear regression method (R²=0.826/0.833) outperformed linear regression methods. This study provides a methodological reference for digital information collection in the fisheries industry.

Abstract:With the rapid development of artificial intelligence, modern fish biology research technology has been constantly updated. Automated and intelligent fish identification will help promote modern fish biology research development. The contour feature of fish morphology is one of the important features of fish recognition. Fish morphology is diversed, and the contour features of fish morphology have species specificity. Meanwhile, it serves as an important scientific basis for fish identification and classification. The extraction effects of morphological contour features directly affect the accuracy of automatic fish identification. Therefore, in order to study the effect of computer vision on the automatic extraction of fish morphological contour features, a two-dimensional image of one tail T. obesus was collected in the Pacific Ocean from September to November 2017 for computer vision analysis through the fish image gray level transformation, bilateral filter, binary image processing and contour extraction and other image processing. 8 - direction chain code technology was used to automatically extract the chain code information of fish contour. The morphological information coefficient was calculated by elliptic Fourier transform and the contour reconstruction was carried out. The results revealed that the contour image of tuna could be obtained well after processing, and the chain code information changes with the size of the contour pixel of fish shape, and the contour reconstruction of fish shape changed with the change of harmonic number. The research results showed that the automatic extraction of fish contour features was effective. The morphological coefficients of fish fluctuated greatly in low harmonic number and little in high harmonic number. The low harmonic number transformation had a great influence on the overall contour information of fish, while the high harmonic number transformation had a great influence on the local contour information of fish. The results of this study lay a preliminary foundation for automatic fish recognition and classification, and also provide references for other related automation research.

Abstract:Accurate assessment of the number of organisms in aquaculture ponds is an important requirement in aquaculture. Fish counting methods based on machine vision and near-infrared light can quickly and accurately estimate the biomass in the culture pond under better light environmental conditions, but it is affected by fish overlap, water turbidity, light, bubbles, and other factors. Fish counting method based on sonar camera and echo sounder can effectively investigate the amount of biological resources in the ocean or lake, but it is limited by the area and water depth of the aquaculture pond in the industrial aquaculture. A time domain interval selection method based on coefficient of variation was proposed to solve the problem of error in the application of ultrasonic detection in aquaculture ponds with low depth. The ultrasonic transducer with an emission frequency of 40 kHz was used to count the fish in aquaculture ponds in a cylinder with a volume of 0.115 m³. Aiming at the problem of error in the estimation results of ordinary multiple scattering theory, a time-domain interval selection method based on coefficient of variation was proposed. The method was used to estimate the number of 10-40 fishes in the cylinder without and with aeration. The results showed that the MAE was 0.924 and 1.769 fish, RMSE was 1.148 and 2.054, and the CV between estimated and actual values were 0.995 and 0.983, respectively. The results showed that the error was highly correlated with the selection of the time domain interval, and the variation coefficient and the three evaluation indexes kept consistent with the change trend of the time domain interval. By selecting the appropriate time domain interval and increasing the number of measurements, the accuracy of the counting results could be effectively improved. The research showed that the accuracy of aquatic organism counting could be effectively improved after increasing the number of samples although there were bubbles in the environment after oxygenation, which leads to a decrease in the accuracy of estimation. The experiment proved that the method was still applicable in the scene with oxygenation demand. This study provides a new path for assessing fish density in low-depth and small-area aquaculture ponds by ultrasonic technology.

Abstract:Shrimp is rich in a variety of trace elements and vitamins, and has a substantial nutritional value, it is also be widely recognized as an important ingredient in high-end, well-known cuisine. Among them, the cultural production of Litopenaeus vannamei accounts for about 85% of the total production of shrimp culture, which is an important economic aquaculture object. The active state of L. vannamei reacts its health condition and behavioral situation. Surveying and identifying the activity of L. vannamei is helpful in finding abnormal behavior in aquaculture, to give early warning and take remedial methods promptly, lessen economic losses in aquaculture, and improve the yield and efficiency of aquaculture. Nowadays in the L. vannamei pond aquaculure process, aquaculture personnel often need to monitor the active swimming state of the shrimp by manually pulling the feed tray, then analyzing the overall environment of the aquaculture pond and formulating effective aquaculture breeding strategies. However, due to the complexity of the pond underwater environment, artificial observation experience is limited, so the method of manually observing the active state of L. vannamei has a lot of problems, such as inefficiency limited scope of application, low accuracy, poor real-time performance, high labor intensity and other problems. In order to solve these problems, propose a visual detection method for the activity of L. vannamei based on an improved YOLOv7 tiny network detection model and multi-objective association based on Euclidean distance to quantitatively study the swimming activity status of shrimp. Based on the YOLOv7 tiny network model, the standard convolution was replaced by Conv convolution, and a VoVGSCSPC module was built to replace the original lightweight aggregation module (ELAN-L). The MPDIoU loss function was used instead of the CIoU loss function to reduce the model capacity and improve the model detection accuracy. The position of shrimp in the image was determined by the visual detection results of improved YOLOv7-tiny model and the multi-objective association method based on Euclidean distance, from which the shrimp's swimming displacement, speed and turn angle were calculated to quantify the shrimp's swimming activity status. After validation on the L. vannamei dataset, the results showed that the misdetection rate and omission rate of the improved YOLOv7-tiny model were reduced by 0.62% and 1.05%, respectively, compared with the YOLOv7-tiny model. The inference speed was improved by 17.07%, so the effectiveness of the improved model was verified. Quantitative analysis of the activity of shrimp showed that the more active shrimp corresponded to the higher the activity index value, which was consistent with the actual situation. The study showed that the proposed quantitative detection method could accurately and quickly obtain the swimming activity index, and could efficiently quantify the swimming activity state of L. vannamei on the feed tray, which was of great significance to grasp the health status of L. vannamei and improved the intelligent level of shrimp culture.

Abstract:Deep sea cages are an important part of fishery equipment and play an important role in modern fishery. A self-elevating cage is a special form of a deep-sea cage. In actual production, it can quickly sink to a deeper position below the water surface by various means when a typhoon or red tide comes so as to resist the adverse effects of complex sea conditions. This study carried out the hydrodynamic performance of the self-elevating cage and its supporting system to explore the hydrodynamic factors affecting the self-elevating cage, optimize and improve the cage structure, reduce the loss of cultured fish in extreme working environments, and improve the safety of the cage system. In this study, the effects of different flow rates, different inflow angles, different dive depths, and different types of netting on the stress of steel structure self-elevating cages were investigated through physical model experiments. The results showed that when the mesh size, the diving depth, and the inflow angle were the same, the overall force of the self-elevating cage was positively correlated with the water flow velocity. When the cage was perpendicular to the water flow direction, the force of the cage was the smallest, and the inflow angle of the cage might be subjected to the maximum impact force at 30 °-60 °. The force of the model cage at 50 cm from the water bottom was greater than that of the cage at 80 cm from the water bottom. The stress of UHMWPE netting was larger than that of PET netting with the same line area. The results showed that the hexagonal mesh PET net could be preferred in the cage design, and the attachment could increase the force of the net to more than 10 times the original. It was suggested that the influence of the attachment on the structural strength of the cage should be fully considered in the design of the cage structure. This study can provide a reference for the design, layout, and netting selection of self-elevating cages. As a kind of deep-sea aquaculture equipment, the prospect of a self-elevating cage is extensive, but the related work has a long way to go.

Abstract:This study employs the Improved Delayed Detached Eddy Simulation (IDDES) method to explore the flow field distributions and hydrodynamic coefficients to further enhance the understanding of the hydrodynamic characteristics of polyethylene terephthalate (PET) nets commonly used in offshore aquaculture facilities. The study examined the effects of inflow velocity, angle of attack, and net solidity on the hydrodynamic behavior of the net panels. Capitalized on a typical case of flowing past a circular cylinder, the accuracies of the turbulence model and the solver were successfully substantiated. The results presented that the velocity fields and hydrodynamic coefficients of the 19 net-mesh panels were close to the full-scale nets. The drag coefficients gradually declined as the increase of inflow velocities. The local flow acceleration effected on both spanwise directions of twines diminish, while the downstream wake gets fully developed. Furthermore, the drag coefficients of nets present a gradual increasing tendency with net solidities, the local flow acceleration effected on both spanwise directions of twines, and the wake interactions were enhanced. The downstream streamlines converged to form a considerable region with low velocities for the case of the solidity 0.230. The drag coefficients continuously increased as the panels became more perpendicular to the flow direction, while the lift coefficients were inclined to rise initially and then decreased. The more considerable velocity reductions in addition to low flow interactions among twines were also observed. The effects of local interactions of flow fields among twines, along with the flow acceleration on both spanwise directions of twines, were the most dominant especially in the case of angle of attack 90°. This study demonstrated that both solidity and angle of attack significantly influenced the flow field distribution and hydrodynamic coefficients of PET nets. This numerical study has clarified the velocity fields and hydrodynamic coefficients around PET nets, providing the solid foundation for further analysis of the flow exchange across aquaculture structures.

Abstract:The massive attachment of fouling organisms will clog the net of fish cages, reduce the water exchange within the cages, and increase the structural load and deformation, which will lead to an increased risk of damage to the cage under severe sea conditions, and may cause significant economic losses. This study conducted a field test by deploying nets in marine cage aquaculture areas, obtaining nets with varying levels of biofouling (Sn=0.213-0.442) to study the effects of fouling organisms on the hydrodynamic characteristics of fish cage nets. The composition of the fouling organisms was analyzed, and flume tests were conducted to explore the hydrodynamic characteristics of hydroid-fouled nets for different flow velocities (u=0.2-0.5 m/s) and angles of attack (θ=0°-90°). The results indicated that attachment of fouling organisms initially increased and then decreased with the duration of immersion time, with the maximum attachment occurring at a depth of 4.5 meters during the fourth week. There was a positive correlation between the wet weight of the net and the degree of attachment. Notably, the attachment of fouling organisms like hydroids significantly alters the hydrodynamic characteristics of the nets. The net with the most severe fouling (S_n=0.442) exhibited a 6.09-fold increase in maximum drag force and a 5.99-fold increase in maximum lift force compared to a clean net (S_n=0.146). The influence of hydroid-fouled nets on their hydrodynamic coefficients under varying angles of attack exhibits notable differences, the drag coefficient could increase by as much as 2.1 times, and correspondingly, the lift coefficient underwent a 2.0-fold enhancement, in comparison to clean nets. At an angle of attack of 90°, the relationship between the drag coefficient (C_d) and solidity ratio (S_n) of the hydroid-fouled nets was well-fitted by the equation C_d =0.42+8.98S_n–7.78S_n2 (R2=0.803, S_n=0.145–0.442). Our research showed fouling organisms, like hydroids, significantly affected the hydrodynamic loads on fish cage nets, worsening stress distribution, and increasing the risk of damage to the net. Therefore, in the design and safety assessment of cages, it was essential to comprehensively consider the impact of changes in net drag force and lift force on the structural integrity of the fish cage. Moreover, fouling organisms should be removed from the nets promptly to ensure structural integrity and safety during the production process of cage aquaculture. This study provides valuable insights for the design and optimization of fish cages, as well as for the scheduling of net cleaning practices. By addressing these aspects, we can enhance the durability of cage net systems, ultimately contributing to more sustainable and efficient aquaculture.

Abstract:A high-precision path tracking control method considering delay characteristics was proposed to improve the path tracking accuracy of aquaculture transport unmanned vehicle in the process of transporting aquatic products. Firstly, the dynamics of the unmanned vehicle in the breeding workshop was analyzed. The dynamics model of the unmanned vehicle was built. Secondly, the communication delay and actuator delay were expressed as pure delay module and first order inertia delay model. The delay dynamic model was constructed. Through the model predictive control algorithm, the controller suitable for the intelligent breeding workshop scene was designed. The simulation platform was built by MATLAB/Simulink and CarSim, and verified based on the real transport unmanned vehicle and workshop layout. The results showed that compared with MPC controller without delay, MPC controller only considering actuator delay and LQR controller considering delay, the lateral error and course Angle error of the proposed method were reduced by 96% and 95%, 15% and 34%, 5% and 28%, respectively. The results showed that the proposed method performed better in path tracking when facing the delay problem. This study solves the delay problem of unmanned vehicles in aquaculture transportation, improves the path tracking accuracy, and ensures the accuracy and safety of unmanned vehicle transportation in aquaculture workshops.

Abstract:Cage aquaculture originated in China and then spread throughout the world gradually. In the late 1970s, there was a new development in the methods and species of cage aquaculture in freshwater cages in China. Floating cage is the most widely used aquaculture method. Floating cage require a minimum size of stocking fry, which must be cultivated to a suitable size before they can be cultured in the cage. In addition, with the growth of the fish, it is necessary to replace the cage with a larger mesh in time. After the cage is in the water for a period of time, it can easily be blocked by algae adhesion. In order to solve the problem that fry in the existing cage culture needs early cultivation and high frequency of cleaning and replacement of floating cage and fixed cage when experimental fish are cultivated, a double layer submerged cage was developed for the breeding aquaculture of fry and fingerling in the pond. The cage net was composed of an upper large mesh size (0.6 cm) and a lower small mesh size (0.42 mm). The fry was cultivated in the lower small mesh net, whereas the fingerling was cultivated in the enclosed space of the upper large mesh net and the lower small mesh net with a gradual increase of water. Through the comparison between double layers cage and floating cage in pond aquaculture, the results showed that the fry and fingerling could be continuously cultured in a double layers cage, while the large-scale fry (full length >2.0 cm) and fingerling could be cultured in a floating cage. The total length, body length, body weight, weight gain and specific growth rate of fingerling fish cultured in double layers cages were significantly higher than those in floating cages (P<0.05). During the three-month breeding period, the double layers cage did not need to be replaced and cleaned, and the floating cage was to be replaced five times and cleaned six times. The results showed that the culture effect of the first-year-old fish in the double layers cage was significantly better than that in the floating cage, and the intensity of culture management was significantly reduced. To verify the breeding effect of this system, six double layers cages with the same specifications were installed in a 660 m² pond in parallel. The saline-alkali tolerant breeding families of Luciobarbus capito were selected as breeding objects, and culturing from fry to fingerling was completed without replacing or cleaning the cage. The body length was 13.87-16.07 cm, and the weight was 34.71-42.07 g, which was a similar size to the one-year-old L. capito obtained under pond aquaculture conditions. Moreover, six families showed different growth characteristics (P<0.05). The research showed that the same breeding conditions and accurate growth data can be provided for the fish family selection by the double layers cage aquaculture in the pond. The double layers cage can provide a pond aquaculture method for fish selective breeding.

Abstract:An EDEM simulation analysis of Ctenopharyngodon idella feed pellets based on the Hertz-Mindlin with JKR model was carried out to study the movement and binding conditions of C. idella feed pellets during the conditioning process and optimize feed conditioning conditions. Firstly, a wet sticky model of feed pellets was established using the discrete element theory. Secondly, the impact of conditioning shaft rotational speed and impeller blade installation angle on feed conditioning effect was analyzed by establishing a simulation control group of two factors, conditioning shaft rotational speed and impeller blade installation angle. Then, according to the feed conditioning characteristics requirements, the optimal combination of shaft rotational speed and impeller blade installation angle was determined. Finally, the conclusion was reached through theoretical calculation and simulation analysis: when the conditioning machine's shaft rotational speed was 250 r/min and impeller blade installation angle was 30°, respectively. The feed pellets were not easily attached to the chamber wall, the materials could mix evenly, and the best conditioning effect could be obtained. The results indicated that the conditioning effect of feed conditioner was closely associated with the blade installation angle and the rotational speed of the shaft. This study can offer a theoretical foundation and guidance for feed manufacturers in optimizing and improving conditioning effects.

Abstract:This study utilized Ruditapes philippinarum as the research subject to develop integrated grading and weighing equipment featuring drum screen grading coupled with automatic weighing to address the issues of low accuracy in grading and weighing R. philippinarum, as well as insufficient procedural coordination. Utilizing discrete element simulation , the grading and weighing processes of R. philippinarum were simulated and analyzed. The impact of roller screen rotational speed, roller screen tilt angle, and feed capacity on grading and weighing accuracies was investigated using response surface analysis in Design-Expert software. This study aimed to optimize the operational parameters of the complete equipment and validate its performance. The findings indicated that the primary factors affecting the accuracy of grading and weighing R. philippinarum, in descending order of influence, were the roller screen tilt angle, roller screen rotational speed, and the feed capacity. The device achieved a grading accuracy of 96.09% and a weighing accuracy of 98.79% under operational conditions of a roller screen rotational speed of 17 r/min, a roller screen tilt angle of 3°, and a feed capacity of 200 kg/h, . After completing the prototype manufacturing and operational validation, the grading accuracy was recorded at 96.56%, and the weighing accuracy at 98.16%. The results demonstrated that setting appropriate parameters for the roller screen tilt angle, roller screen rotational speed, and feed capacity could significantly enhance grading and weighing accuracies in the process of handling R. philippinarum. This research provides valuable insights for the design of post-harvest grading and weighing devices for shellfish.