• Electronics and Telecommunications Trends
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• v.38 no.3
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• pp.29-37
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• 2023

With the increasing demand for seafood and technological advancement in aquaculture, the industry has continuously grown. On the other hand, digital twins have been actively applied to various industries. Aquaculture deals with live aquatic animals that are sensitive to growth environment management. Hence, applying a digital twin to smart aquaculture may lead to a substantial economic benefit because it enables the optimization of different variables. We analyze the status of digital twin development in agriculture. The services of the aquafarm digital twin are divided into 1) data management, 2) optimization, and 3) intelligence. Standardization related to the aquafarm digital twin is also discussed. Based on the analyses, the development stage of aquafarm digital twin is defined, and directions of technology development are suggested.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.11a
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• pp.125-126
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• 2019

스마트농장과 스마트양식장으로 구분하여 첨단 정보통신기술(ICT), 사물인터넷(IoT), 인공지능(AI) 그리고 빅데이터 등이 적용된 국내외 스마트농장과 스마트양식장 사례와 해수를 이용한 해수온실의 사례 그리고 플로팅 팜과 스마트 플로팅 팜의 계획안 및 실제 사례를 분석했다. 사례분석을 통해 스마트 플로팅 팜에 적용되는 다양한 종류의 시스템을 분류하여 해수복합형 시스템 개념을 도출해냈다.

• Fisheries and Aquatic Sciences
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• v.25 no.3
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• pp.151-157
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• 2022

Various approaches have been applied to transform aquaculture from a manual, labour-intensive industry to one dependent on automation technologies in the era of the fourth industrial revolution. Technologies associated with the monitoring of physical condition have successfully been applied in most aquafarm facilities; however, real-time biological monitoring systems that can observe fish condition and behaviour are still required. In this study, we used a video recorder placed on top of a fish tank to observe the swimming patterns of rock bream (Oplegnathus fasciatus), first one fish alone and then a group of five fish. Rock bream in the video samples were successfully identified using the you-only-look-once v3 algorithm, which is based on the Darknet-53 convolutional neural network. In addition to recordings of swimming behaviour under normal conditions, the swimming patterns of fish under abnormal conditions were recorded on adding an anaesthetic or lowering the salinity. The abnormal conditions led to changes in the velocity of movement (3.8 ± 0.6 cm/s) involving an initial rapid increase in speed (up to 16.5 ± 3.0 cm/s, upon 2-phenoxyethanol treatment) before the fish stopped moving, as well as changing from swimming upright to dying lying on their sides. Machine learning was applied to datasets consisting of normal or abnormal behaviour patterns, to evaluate the fish behaviour. The proposed algorithm showed a high accuracy (98.1%) in discriminating normal and abnormal rock bream behaviour. We conclude that artificial intelligence-based detection of abnormal behaviour can be applied to develop an automatic bio-management system for use in the aquaculture industry.