• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.56 no.1
• /
• pp.116-123
• /
• 2023

Flow-through aquaculture systems generate large amounts of wastewater containing compounds such as solids that can settle near aquafarms and cause eutrophication. The settled solids are often reintroduced into flow-through systems, and aquatic animals can be affected by the solids and pathogens associated with these solids. For a sustainable aquaculture operation, adequate wastewater treatment is required. Hydrocyclones are one of the most promising technologies for the removal of solids in aquaculture wastewater. In this study, a model for performance prediction of hydrocyclones was investigated under three different operating conditions: water temperature, solids concentration, and water inlet velocity. The synthetic solids solution was prepared using settled solids from abalone aquaculture farms. The daily solids removal rates of the tested hydrocyclones ranged from 0.18 to 26.0 g solids-m^-3-day^-1, and removal efficiency ranged from 5.1 to 34.4%. The inlet water velocity had the greatest effect on solids removal and hydrocyclone efficiencies. The following multiregression model equation was derived from the daily solids removal rate (g solids-m^-3-day^-1) results for water temperature (T, ℃), solids concentration (SS, mg-L^-1), and tangential inlet water velocity (TIV, m-sec^-1): daily solids removal rate: f(z)=4.465+0.809TIV-0.375T+0.217SS (r²=0.976).

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.56 no.4
• /
• pp.395-406
• /
• 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
• /
• v.8 no.2
• /
• pp.73-82
• /
• 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
• /
• v.21 no.1
• /
• pp.552-560
• /
• 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
• /
• v.15 no.5
• /
• pp.959-966
• /
• 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
• /
• v.2 no.3
• /
• pp.31-35
• /
• 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
• /
• v.11 no.4
• /
• pp.56-61
• /
• 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
• /
• v.55 no.3
• /
• pp.328-337
• /
• 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
• /
• v.13 no.6
• /
• pp.1379-1386
• /
• 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.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.5
• /
• pp.883-894
• /
• 2024

This study aims to develop an AI-based aquaculture water quality monitoring and control system. Reliable and durable sensors were developed through the design of embedded boards and PCB fabrication, and communication modules were integrated for data collection and transmission. Water quality data from various tanks were collected and analyzed using machine learning techniques to build predictive and control models for water quality changes. The results showed that the AI-based water quality control system demonstrated high prediction accuracy and was effective in real-time monitoring and controlling the water quality.