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Application of image identification to UAV control for cage culture

The purpose of this study was to save manpower and reduce costs on water quality measurement in cage culture. An unmanned aerial vehicle system was applied to locate the target net cage and detect the water quality and temperature in the desired cage automatically. This paper presents the use of ima...

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Detalles Bibliográficos
Autores principales: Liang, Wei-Yi, Juang, Jih-Gau
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358577/
https://www.ncbi.nlm.nih.gov/pubmed/36384336
http://dx.doi.org/10.1177/00368504221135450
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author Liang, Wei-Yi
Juang, Jih-Gau
author_facet Liang, Wei-Yi
Juang, Jih-Gau
author_sort Liang, Wei-Yi
collection PubMed
description The purpose of this study was to save manpower and reduce costs on water quality measurement in cage culture. An unmanned aerial vehicle system was applied to locate the target net cage and detect the water quality and temperature in the desired cage automatically. This paper presents the use of image recognition and deep learning to find a predefined target location of cage aquaculture. The whole drone control and image recognition process was based on an onboard computer and was successfully realized in an actual environment. When the drone approached the net cage, image recognition was utilized to fix the position of the unmanned aerial vehicle on the net cage and drop a sensor to check the water quality. The proposed system could improve conventional manned measurement methods and reduce the costs of cage culture.
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spelling pubmed-103585772023-08-09 Application of image identification to UAV control for cage culture Liang, Wei-Yi Juang, Jih-Gau Sci Prog Conference Collection: IMETI 2021 The purpose of this study was to save manpower and reduce costs on water quality measurement in cage culture. An unmanned aerial vehicle system was applied to locate the target net cage and detect the water quality and temperature in the desired cage automatically. This paper presents the use of image recognition and deep learning to find a predefined target location of cage aquaculture. The whole drone control and image recognition process was based on an onboard computer and was successfully realized in an actual environment. When the drone approached the net cage, image recognition was utilized to fix the position of the unmanned aerial vehicle on the net cage and drop a sensor to check the water quality. The proposed system could improve conventional manned measurement methods and reduce the costs of cage culture. SAGE Publications 2022-11-16 /pmc/articles/PMC10358577/ /pubmed/36384336 http://dx.doi.org/10.1177/00368504221135450 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Conference Collection: IMETI 2021
Liang, Wei-Yi
Juang, Jih-Gau
Application of image identification to UAV control for cage culture
title Application of image identification to UAV control for cage culture
title_full Application of image identification to UAV control for cage culture
title_fullStr Application of image identification to UAV control for cage culture
title_full_unstemmed Application of image identification to UAV control for cage culture
title_short Application of image identification to UAV control for cage culture
title_sort application of image identification to uav control for cage culture
topic Conference Collection: IMETI 2021
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10358577/
https://www.ncbi.nlm.nih.gov/pubmed/36384336
http://dx.doi.org/10.1177/00368504221135450
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