Measuring feeding activity of fish in RAS using computer vision
In recirculating aquaculture systems (RAS), over-feeding events impact the welfare of Atlantic salmon and the operation of the aquaculture system. This study describes a computer vision-based method for measuring the feeding activity of an Atlantic salmon (Salmo salar) shoal. The feeding motions of...
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ftchinacasciocas:oai:ir.qdio.ac.cn:337002/24233 2023-05-15T15:29:09+02:00 Measuring feeding activity of fish in RAS using computer vision Liu, Ziyi Li, Xian Fan, Liangzhong Lu, Huanda Liu, Li Liu, Ying Liu, Y (reprint author), Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China. 2014-05-01 http://ir.qdio.ac.cn/handle/337002/24233 https://doi.org/10.1016/j.aquaeng.2014.03.005 英语 eng AQUACULTURAL ENGINEERING http://ir.qdio.ac.cn/handle/337002/24233 doi:10.1016/j.aquaeng.2014.03.005 cn.org.cspace.api.content.CopyrightPolicy@21c7bd64 Computer Vision Atlantic Salmon Shoal Feeding Activity Recirculating Aquaculture Systems Agriculture Fisheries Article 期刊论文 2014 ftchinacasciocas https://doi.org/10.1016/j.aquaeng.2014.03.005 2022-06-27T05:36:52Z In recirculating aquaculture systems (RAS), over-feeding events impact the welfare of Atlantic salmon and the operation of the aquaculture system. This study describes a computer vision-based method for measuring the feeding activity of an Atlantic salmon (Salmo salar) shoal. The feeding motions of Atlantic salmon were recorded with a CCD camera mounted above the rearing tank. Feeding activity analysis was based on the intensity summation of the difference frame due to the motions of the fish. An overlap coefficient was defined to calibrate the error of calculation caused by the overlaps among fish bodies in images. An approach to filter out the influence of water surface reflections on preceding results was proposed. Based on these data, a computer vision-based feeding activity index (CVFAI) was determined for measuring the feeding activity of the fish in arbitrary given duration. To assess the reliability of CVFAI, a manual observation feeding activity index (MOFAI) was determined by scoring each kind of feeding behavior in the same recordings. The CVFAI and MOFAI presented a linear relationship at a correlation coefficient of 0.9195. CVFAI is therefore a potential indicator for measuring the feeding activity of an Atlantic salmon shoal, in a low-cost and rapid way. (C) 2014 Elsevier B.V. All rights reserved. Article in Journal/Newspaper Atlantic salmon Salmo salar Institute of Oceanology, Chinese Academy of Sciences: IOCAS-IR Aquacultural Engineering 60 20 27 |
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Open Polar |
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Institute of Oceanology, Chinese Academy of Sciences: IOCAS-IR |
op_collection_id |
ftchinacasciocas |
language |
English |
topic |
Computer Vision Atlantic Salmon Shoal Feeding Activity Recirculating Aquaculture Systems Agriculture Fisheries |
spellingShingle |
Computer Vision Atlantic Salmon Shoal Feeding Activity Recirculating Aquaculture Systems Agriculture Fisheries Liu, Ziyi Li, Xian Fan, Liangzhong Lu, Huanda Liu, Li Liu, Ying Liu, Y (reprint author), Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China. Measuring feeding activity of fish in RAS using computer vision |
topic_facet |
Computer Vision Atlantic Salmon Shoal Feeding Activity Recirculating Aquaculture Systems Agriculture Fisheries |
description |
In recirculating aquaculture systems (RAS), over-feeding events impact the welfare of Atlantic salmon and the operation of the aquaculture system. This study describes a computer vision-based method for measuring the feeding activity of an Atlantic salmon (Salmo salar) shoal. The feeding motions of Atlantic salmon were recorded with a CCD camera mounted above the rearing tank. Feeding activity analysis was based on the intensity summation of the difference frame due to the motions of the fish. An overlap coefficient was defined to calibrate the error of calculation caused by the overlaps among fish bodies in images. An approach to filter out the influence of water surface reflections on preceding results was proposed. Based on these data, a computer vision-based feeding activity index (CVFAI) was determined for measuring the feeding activity of the fish in arbitrary given duration. To assess the reliability of CVFAI, a manual observation feeding activity index (MOFAI) was determined by scoring each kind of feeding behavior in the same recordings. The CVFAI and MOFAI presented a linear relationship at a correlation coefficient of 0.9195. CVFAI is therefore a potential indicator for measuring the feeding activity of an Atlantic salmon shoal, in a low-cost and rapid way. (C) 2014 Elsevier B.V. All rights reserved. |
format |
Article in Journal/Newspaper |
author |
Liu, Ziyi Li, Xian Fan, Liangzhong Lu, Huanda Liu, Li Liu, Ying Liu, Y (reprint author), Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China. |
author_facet |
Liu, Ziyi Li, Xian Fan, Liangzhong Lu, Huanda Liu, Li Liu, Ying Liu, Y (reprint author), Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Peoples R China. |
author_sort |
Liu, Ziyi |
title |
Measuring feeding activity of fish in RAS using computer vision |
title_short |
Measuring feeding activity of fish in RAS using computer vision |
title_full |
Measuring feeding activity of fish in RAS using computer vision |
title_fullStr |
Measuring feeding activity of fish in RAS using computer vision |
title_full_unstemmed |
Measuring feeding activity of fish in RAS using computer vision |
title_sort |
measuring feeding activity of fish in ras using computer vision |
publishDate |
2014 |
url |
http://ir.qdio.ac.cn/handle/337002/24233 https://doi.org/10.1016/j.aquaeng.2014.03.005 |
genre |
Atlantic salmon Salmo salar |
genre_facet |
Atlantic salmon Salmo salar |
op_relation |
AQUACULTURAL ENGINEERING http://ir.qdio.ac.cn/handle/337002/24233 doi:10.1016/j.aquaeng.2014.03.005 |
op_rights |
cn.org.cspace.api.content.CopyrightPolicy@21c7bd64 |
op_doi |
https://doi.org/10.1016/j.aquaeng.2014.03.005 |
container_title |
Aquacultural Engineering |
container_volume |
60 |
container_start_page |
20 |
op_container_end_page |
27 |
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1766359526485786624 |