• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.4
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• pp.395-406
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• 2020

The objectives of this study were to develop the optimal structures of recirculating aquaculture tank for improving the removal efficiency of solid materials and maintaining water quality conditions. Flow analysis was performed using the CFD (computational fluid dynamics) method to understand the hydrodynamic characteristics of the circular tank according to the angle of inclination in the tank bottom (0°, 1.5° and 3°), circulating water inflow method (underwater, horizontal nozzle, vertical nozzle and combination nozzle) and the number of inlets. As the angle in tank bottom increased, the vortex inside the tank decreased, resulting in a constant flow. In the case of the vertical nozzle type, the eddy flow in the tank was greatly improved. The vertical nozzle type showed excellent flow such as constant flow velocity distribution and uniform streamline. The combination nozzle type also showed an internal spiral flow, but the vortex reduction effect was less than the vertical nozzle type. As the number of inlets in the tank increased, problems such as speed reduction were compensated, resulting in uniform fluid flow.

• The Journal of Bigdata
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• v.8 no.2
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• pp.73-82
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• 2023

With 78% of current fisheries workers being elderly, there's a pressing need to address labor shortages. Consequently, active research on smart aquaculture technologies, centered on object detection and tracking algorithms, is underway. These technologies allow for fish size analysis and behavior pattern forecasting, facilitating the development of real-time monitoring and automated systems. Our study utilized video data from cameras outside aquaculture facilities and implemented fish detection and tracking algorithms. We aimed to tackle high maintenance costs due to underwater conditions and camera corrosion from ammonia and pH levels. We evaluated the performance of a real-time system using YOLOv7 for fish detection and the SORT algorithm for movement tracking. YOLOv7 results demonstrated a trade-off between Recall and Precision, minimizing false detections from lighting, water currents, and shadows. Effective tracking was ascertained through re-identification. This research holds promise for enhancing smart aquaculture's operational efficiency and improving fishery facility management.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.959-966
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• 2020

With the growing importance of the fishery industry and the rapid growth of the aquaculture industry, research on smart farms through ICT convergence in the aquaculture field is in progress. To enable monitoring of the growing environment at the farm site, an underwater drone drive unit, an image collection device, an integrated controller for posture stabilization, and a remote control device capable of controlling and controlling drones through real-time underwater images were proposed, and design, development, and tests were conducted. By utilizing underwater drones, it is possible to replace the supply and demand of manpower and high-cost work in the aquaculture industry, and to manage fish farms in a stable manner by reducing the probability of farming deaths.

• Journal of Internet of Things and Convergence
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• v.2 no.3
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• pp.31-35
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• 2016

IoT technologies have been rapidly developed in recent years, and applied to many industries. In the field of fisheries, the water quality management system have been developed, helping in improving productivity and working environment. In this paper, we have designed the smart feeding system, interoperable with the water quality system, using LPWA network. LPWA network is an IoT network, which is appropriate to fish farms because of its wide area coverages and low power consumption. We expect this work to contribute to developing the aquaculture technology through the big data analysis with the accumulated data.

• Smart Media Journal
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• v.11 no.4
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• pp.56-61
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• 2022

The purpose of this study is to analyze affecting factors on the income, farming scale, and farming implementation of graduates of the Korea National College of Agriculture and Fisheries using panel model. For this, we used a generalized estimation equation among the panel analysis methods. The factors that have a positive (+) effect on income were men, married people, and successive farmers. In the case of parents' cooperative farming, dairy farming or poultry farming, matching the major at the time of graduation with the main items, the income was also high. Factors that have a positive (+) effect on farming scale were unmarried people, parents' cooperative farming, aquaculture cultivation, and poultry farming. The factors that implemented the mandatory farming implementation well were men, married people, parents' cooperative farming, aquaculture cultivation, and pig farming. Through the results of this study, it will be possible to help manage and support graduates and enrolled students.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.3
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• pp.328-337
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• 2022

This study investigated the removal performance of a foam fractionator under seawater conditions. The foam fractionator was tested using a 3×3×3 factorial design for operating conditions by combining different solids concentrations (SS; 1, 5, and 10 mg·L^-1), surface air velocities (SAV; 1.1, 1.5, and 2.1 cm·sec^-1), and hydraulic residence times (HRT; 1, 3, and 6 min) at 16℃. Performance parameters such as daily solids removal rate and efficiency were measured, and a multi-regression model equation was developed accordingly. The daily solids removal rate and removal efficiency varied with the experimental conditions and ranged from 0.14-2.33 g-solids·m^-3-air·day^-1 and 8.9-96.7 %, respectively. Overall, the daily solids removal rate increased with increasing SS and SAV and decreasing HRT, whereas the removal efficiency increased with increasing SAV and HRT and decreasing SS. The daily solids removal rate (g-solids·m^-3-air·day^-1) of the foam fractionator for SAV (cm·sec^-1), SS (mg·L^-1) and HRT (min) were described by the following multi-regression model: Daily solids removal rate [f(z)]=-0.118+0.422SAV+0.094HRT+0.141SS (r²=0.873).

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1379-1386
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• 2018

Conventional underwater cameras used in fish farms can only shoot limited areas and are vulnerable to underwater contamination. There is also a problem with contaminated farms as surplus residues are deposited as a result of feed supply to farms' nets. This paper proposes underwater drones for underwater monitoring of fish farms and cleaning nets. If underwater drones are used for management of fish farms, underwater imaging, monitoring and cleaning of fish farms' nets can be possible. By using this technology, data can be collected by detecting changes in the environment of a fish farm and responding to changes that occur within a fish farm based on the data. In addition, the establishment of an integrated control system will enable to build efficient and stable smart farms.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.41-47
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• 2023

Today, due to the increase in global population growth, the international community is discussing solving the food problem. The aquaculture industry is emerging as an alternative to solving the food problem. For the innovative growth of the aquaculture industry, smart fish farms that combine the fourth industrial technology are recently being distributed, and full-cycle digitalization is being promoted. Water quality sensors, which are important in the aquaculture industry, are electrochemical portable sensors that check water quality individually and intermittently, making it impossible to analyze and manage water quality in real time. Recently, optically-based monitoring sensors have been developed and applied, but the reliability of monitoring data cannot be guaranteed because the state information of the water quality sensor is unknown. Therefore, this paper proposes an algorithm representing self-diagnosis status such as Failure, Out of Specification, Maintenance Required, and Check Function based on monitoring data collected by water quality sensors to ensure data reliability.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.208-216
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• 2023

The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.