• Title/Summary/Keyword: 양식장 관리

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Measuring the Quantities of Aquaculture Farming Facilities for Seaweed, Ear Shell and Fish Using High Resolution Aerial Images - A Case of the Wando Region, Jeollanamdo - (고해상 항공영상을 활용한 김, 전복, 어류 양식장 시설량의 산출 - 전라남도 완도지역을 대상으로 -)

  • Jo, Myung-Hee
    • Journal of the Korean Association of Geographic Information Studies
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    • v.14 no.2
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    • pp.147-161
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    • 2011
  • Korea is surrounded by sea on three sides. This country has been supplied with a variety of aquaculture products cultivated on shores. There have recently been a lot of studies to have better understanding of the correct location and quantity of aquaculture farms for seaweed, ear shells and fish that cover a wide area of sea. And it is necessary to use the geographic information system and remote sensing to detect the aquaculture farms in order to effectively manage them. This study uses higher resolution aerial images(25 centimeters) than satellite images of 2~2.5-meter resolution that have been ever used, to conduct an accuracy detection of aquaculture farming facilities. It chooses as the case study area the Wando region that has aquaculture farms for seaweed, ear shells and fish. Aerial photos of the island were obtained in this study and an image correction of them was conducted. A spatial database was then constructed in this study and the detection of aquaculture farming facilities was performed. An analysis of facilities inside and outside the permitted areas reveals that there has been an increase in the facilities of seaweed and ear shell aquaculture farms outside the permitted areas. And also it tells that because the facilities of fish aquaculture farms have turned into those of ear shell aquaculture farms, there has been a decrease in permitted facilities, facilities detected on the basis of aerial images, and facilities outside the permitted area. It will be necessary to continuously control and manage the unpermitted facilities, regarding the increase in the facilities inside and outside the permitted area for seaweed and ear shell aquaculture farms. Because the facilities of aquaculture farms cover a wide range of areas(sea) in this manner, it is more effective to depend on high resolution aerial images than a field survey to detect and calculate the facilities. This study comes up with a plan for using aerial images to detect the location and the quantity of the fish aquaculture facilities and then effectively manage them.

Multifunctional Marine Exploration Robot for Fish Farm Management (양식장 관리를 위한 다기능 해양탐사 로봇)

  • Yeo, Sang-Sam;Park, Joo-Ryeoll;Lee, Dong-Kyu;Kim, Myeong-Gi;Yu, Ju-Young;Lee, Sang Hyeop
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2021.07a
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    • pp.489-490
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    • 2021
  • 본 연구에서는 해양사고 처리 중 발생하는 인명사고 발생률을 감소시키고, 효율적으로 양식장을 관리하는 것을 전제로 카메라와 센서를 다기능 해양탐사 로봇에 적용하고자 한다. 현재의 양식장 관리 시스템은 수온 체크만 할 수 있게 되어있다. 이러한 시스템은 양식어에게 적합한 환경을 제공해주기 쉽지 않다. 본 논문은 이러한 문제점들을 개선하기 위해 기존의 해양 처리시스템과 양식 시스템 대신 카메라와 수온 센서, pH 농도 센서, 초음파 거리 센서, DC 모터, 블루투스 모듈을 적용한 다기능 해양탐사 로봇 기술을 제안한다. 기존의 시스템과는 다르게 안전하고 효율적으로 환경을 분석하고, 제어할 수 있다.

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Facilities Analysis of Laver Cultivation Grounds in Korean Coastal Waters Using SPOT-5 Images in 2005 (SPOT-5 위성영상에 의한 2005년 한국 연안 김 양식장의 시설현황 분석)

  • Yang Chan-Su;Park Sung-Woo
    • Journal of the Korean Society for Marine Environment & Energy
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    • v.9 no.3
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    • pp.168-175
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    • 2006
  • The cultural grounds of lave r have been surveyed using SPOT-5 satellite images. The facilities of laver cultivation area in the coastal waters of Korea were calculated. 10 m resolution multispectral images of SPOT-5 are adopted for the southern are a of Jebu Island, Hwaseong city to develop an automatic detection approach of laver nets that consists of the following: band difference technique, canny edge detector and morphological analysis: The number of satellite-based facilities was relatively high as compared with the licensed number in 2005, 676,749 chaek and 572,745 chaek(柵, unit of measure for laver farm), respectively. The ratio of a law abiding facility was very low at 52.9%. These data could be applied to control its national production keeping a stable market price for the government body.

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Development of Bivalve Culture Management System based on GIS for Oyster Aquaculture in GeojeHansan Bay (거제한산만 굴 양식장에 대한 GIS 기반 어장관리시스템 개발)

  • Cho, Yoon-Sik;Hong, Sok-Jin;Kim, Hyung-Chul;Choi, Woo-Jeung;Lee, Won-Chan;Lee, Suk-Mo
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.16 no.1
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    • pp.11-20
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    • 2010
  • Oyster production is playing an important role in domestic aquaculture, but facing some problems such as exports decrease, a slowdown in domestic demand and marine environmental deterioration. In order to obtain the suitable and sustainable oyster production, suitable sites selection is an important step in oyster aquaculture. This study was conducted to identify the suitable sites for lunging culture of oyster using Geographic Information System(GIS)-based multi-criteria evaluation methods. Most of the parameters were extracted by Inverse Distance Weighted(IDW) methods in GIS and eight parameters were grouped into two basic sub-models for oyster aquaculture, namely oyster growth sub-model(Sea Temperature, Salinity, Hydrodynamics, Chlorophyll-a) and environment sub-model(Bottom DO, TOC, Sediment AVS, Benthic Diversity). Suitability scores were ranked on a scale from 1(leased suitable) and 8(most suitable), and about 80.1% of the total potential area had the highest scores 5 and 6. These areas were shown to have the optimum condition for oyster culture in GeojeHansan Bay. This method to identify suitable sites for oyster culture may be used to develop bivalve culture management system for supporting a decision making.

양식어류의 선별과정중 수심감소와 어류의 수조이동에 따른 스트레스 반응

  • 허준욱;장영진;임한규;이복규
    • Proceedings of the Korean Society of Fisheries Technology Conference
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    • 2001.05a
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    • pp.285-286
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    • 2001
  • 양식장에서 빈번하게 발생할 수 있는 스트레스 요인은 인위적 및 환경적 요인으로 나뉘어지며, 어류의 성장과 항상성 유지에 상당한 영향을 미치는 것으로 알려져 있다(Pickering, 1992). 인위적 스트레스 요인중 성장차이가 나는 어류를 같은 크기의 그룹으로 조절하는 선별작업은 양식장에서 피할 수 없는 관리사항의 하나이며, 빈번하고도 난잡한 선별작업은 어류에게 상당한 스트레스 요인으로 작용할 것이다. (중략)

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Water quality data analysis for development of artificial intelligence-based fish farm management system (인공지능 기반(ML) 양식장 관리시스템 개발을 위한 수질 데이터 분석)

  • Hyun Sim;Heung Sup Sim
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2023.01a
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    • pp.205-208
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    • 2023
  • 양식장에서 최적의 생육환경을 유지할 수 있는 제어시스템 개발을 위해 수질에 영향을 미치는 요인들의 상관관계 분석을 위한 머신러닝 모델을 개발하고자 한다. 데이터간의 상관관계 분석 및 예측모델 생성을 위해 알고리즘의 결정계수와 MSE, RMSE 등의 수치를 통하여 데이터의 적합성을 검증하고자 한다.

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TGC-based Fish Growth Estimation Model using Gaussian Process Regression Approach (가우시안 프로세스 회귀를 통한 열 성장 계수 기반의 어류 성장 예측 모델)

  • Juhyoung Sung;Sungyoon Cho;Da-Eun Jung;Jongwon Kim;Jeonghwan Park;Kiwon Kwon;Young Myoung Ko
    • Journal of Internet Computing and Services
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    • v.24 no.1
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    • pp.61-69
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    • 2023
  • Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

Satellite Remote Sensing Application: Facilities Analysis of Laver Cultivation Grounds System (인공위성 원격탐사의 활용: 김양식장의 현황 모니터링)

  • Yang, Chan-Su;Moon, Jeong-Eon;Lee, Nu-Ree;Park, Sung-Woo
    • Proceedings of KOSOMES biannual meeting
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    • 2006.05a
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    • pp.47-52
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    • 2006
  • The cultural grounds of laver has been surveyed using SPOT-5 satellite images to calculate the facilities of laver cultivation area in the coastal waters of Korea 10m resolution multispectral images of SPOT-5 are adopted for the south area of Daebu Island, Hwaseong city to develop an automatic detection approach of laver nets that consists of the following: band difference technique, canny edge detector and morphological analysis. The satellite-based facilities number was relatively high as compared with the licensed number in 2005, 676,749 chaek and 572,745 chaek(柵, unit of measure for laver farm), respectively. The data could be applied to achieve a good harvest for laver seaweed growers and to control its national production keeping a stable market price for the government body.

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