• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.2
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• pp.173-181
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• 2014

The Economist was implied most of the major fisheries are procured by aquaculture in 2030 affected by the Aquaculture Revolution. William Hallal was also predicted that amount of aquatic products will be about 50% of the total fishery in 2015. Various organizations had been conducted various u-farm researches and demonstration projects due to changing environment. This study aims to propose an ICT-based technologies and policies for the ICT-based smart system by identifying results and problems.

• 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.

• Korea Information Processing Society Review
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• v.24 no.6
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• pp.36-47
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• 2017

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.247-260
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• 2016

Sensor networks have been used in many applications such as smart home, smart factory, etc. based on sensor data. Sensor networks can change system requirements and architectures depending on their application areas. Currently, sensor network application cases in ocean environments are very rare because the ocean environments have much difficult accessibility more poor conditions, higher wave heights, more frogs, much heavier salinity, etc., compared with ground environments. In this paper, we propose the requirements, architecture and design of a sensor network system for the littoral sea cage culture monitoring and we also introduce its operation results through the development. The developed system based on our research provides users with functionalities to extract, monitor, and manage underwater environmental conditions suitable to littoral sea cage culturing of fishes.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.957-968
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• 2021

To improve the data on reliability of the onshore fish farm water quality monitoring system and operate the system efficiently, the water quality data of the onshore seawater fish farms which are progressing test operation, and the marine environmental information network(Wando tidal station) were compared and analyzed. Furthermore, data validation, data range filters, and data displacement checks were applied to analyze the data in a way that eliminates the data errors in water quality monitoring systems and increases the reliability of measurement data.

• Smart Media Journal
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• v.12 no.10
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• pp.71-84
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• 2023

Paralichthys olivaceus accounts for a large proportion, accounting for more than half of Korea's aquaculture industry. However, about 25-30% of the total breeding volume throughout the year occurs due to diseases, which has a very bad impact on the economic feasibility of fish farms. For the economic growth of Paralichthys olivaceus farms, it is necessary to quickly and accurately diagnose disease symptoms by automating the diagnosis of Paralichthys olivaceus diseases. In this study, we create training data using innovative data collection methods, refining data algorithms, and techniques for partitioning dataset, and compare the Paralichthys olivaceus disease symptom detection performance of four object detection deep learning models(such as YOLOv8, Swin, Vitdet, MvitV2). The experimental findings indicate that the YOLOv8 model demonstrates superiority in terms of average detection rate (mAP) and Estimated Time of Arrival (ETA). If the performance of the AI model proposed in this study is verified, Paralichthys olivaceus farms can diagnose disease symptoms in real time, and it is expected that the productivity of the farm will be greatly improved by rapid preventive measures according to the diagnosis results.

• Smart Media Journal
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• v.7 no.2
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• pp.34-39
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• 2018

There are many studies on red tide due to the continuous increase in damage to domestic fish and shell farms by the harmful red tide. However, there is insufficient domestic research of identifying harmful red tide algae that automatically recognizes red tide images. In this paper, we propose a red tide image classification method using deep learning based open source. To solve the problem of recognition of various images of red tide algae, the proposed method is implemented by using tensorflow framework and Google image classification model.

• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.167-176
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• 2020

The automated fish feed system currently used in Korea supplies a certain amounts of feed to water tanks at a certain time. This automated system can reduce the labor cost of managing aqua farms, but it is very difficult to control intelligently and appropriately the amount of expensive feed that is critical to aqua farm productivity. In this paper, we propose the FIIFF Inference Model( Fuzzy Inference-based Intelligent Fish Feeding Model) that can solves the problems of these existing automatic fish feeding devices and maximizes the efficiency of feed supply while properly maintaining the growth rate of fish in aqua farms. The proposed FIIFF inference model has the advantage of being able to control feed amounts appropriately since it computes the amount of feed using the current water environments and fish activity state of the aqua farms. The result of the feed amount yield experiment with the proposed FIIFF Inference Model represents the effect of saving 14.8% over the eight months of actual feed amount in the aqua farm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.285-286
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• 2021

With the recent development of Internet of Things (IoT) technology, smartization technology is expanding to the fields of agriculture, livestock, and fisheries, and smartization is in progress. In this smart technology, the most important thing is how to measure the data in the field and transmit it to the management system. Currently, the sensors used in the construction of smart farms and other livestock houses and farms are measuring and monitoring smart farms and other environmental conditions through various sensors such as temperature, humidity, CO gas, CO2, hydrogen, and O2. The communication method between these sensors and the HMI (Human Machine Interface) module that controls and manages the smart farm is still mainly using the RS-485-based modbus-RTU method. In this paper, we intend to design the MCU module for HMI so that various sensor modules can be connected to manage data through the RS-485-based Modbus method so that the sensor data required for smart farm construction can be managed by the HMI module.