AN AUTOMATIC DETECTION METHOD FOR ABNORMAL LAYING HEN ACTIVITIES USING A 3D DEPTH CAMERA
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Engenharia Agrícola |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162021000300263 |
Resumo: | ABSTRACT With the increasing scale of farms and the correspondingly higher number of laying hens, it is increasingly difficult for farmers to monitor their animals in a traditional way. Early warning of abnormal animal activities is helpful for farmers’ fast response to the negative impact on animal health, animal welfare and daily management. This study introduces an automatic and non-invasive method for detecting abnormal poultry activities using a 3D depth camera. A typical region including eighteen Hy-line brown laying hens was continuously monitored by a top-view Kinect during 49 continuous days. A mean prediction model (MPM), based on the frame difference algorithm, was built to monitor animal activities and occupation zones. As a result, this method reported abnormal activities with an average accuracy of 84.2% and a rate of misclassifying abnormal events of 15.8% (PFPR). Additionally, it was found that the flock showed a diurnal change pattern in the activity and occupation quantified index. They also presented a similar changing pattern each week. |
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Engenharia Agrícola |
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AN AUTOMATIC DETECTION METHOD FOR ABNORMAL LAYING HEN ACTIVITIES USING A 3D DEPTH CAMERAActivityoccupation index3D depth cameraMPMlaying hensABSTRACT With the increasing scale of farms and the correspondingly higher number of laying hens, it is increasingly difficult for farmers to monitor their animals in a traditional way. Early warning of abnormal animal activities is helpful for farmers’ fast response to the negative impact on animal health, animal welfare and daily management. This study introduces an automatic and non-invasive method for detecting abnormal poultry activities using a 3D depth camera. A typical region including eighteen Hy-line brown laying hens was continuously monitored by a top-view Kinect during 49 continuous days. A mean prediction model (MPM), based on the frame difference algorithm, was built to monitor animal activities and occupation zones. As a result, this method reported abnormal activities with an average accuracy of 84.2% and a rate of misclassifying abnormal events of 15.8% (PFPR). Additionally, it was found that the flock showed a diurnal change pattern in the activity and occupation quantified index. They also presented a similar changing pattern each week.Associação Brasileira de Engenharia Agrícola2021-05-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162021000300263Engenharia Agrícola v.41 n.3 2021reponame:Engenharia Agrícolainstname:Associação Brasileira de Engenharia Agrícola (SBEA)instacron:SBEA10.1590/1809-4430-eng.agric.v41n3p263-270/2021info:eu-repo/semantics/openAccessDu,XiaodongTeng,Guanghuieng2021-06-23T00:00:00Zoai:scielo:S0100-69162021000300263Revistahttp://www.engenhariaagricola.org.br/ORGhttps://old.scielo.br/oai/scielo-oai.phprevistasbea@sbea.org.br||sbea@sbea.org.br1809-44300100-6916opendoar:2021-06-23T00:00Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA)false |
dc.title.none.fl_str_mv |
AN AUTOMATIC DETECTION METHOD FOR ABNORMAL LAYING HEN ACTIVITIES USING A 3D DEPTH CAMERA |
title |
AN AUTOMATIC DETECTION METHOD FOR ABNORMAL LAYING HEN ACTIVITIES USING A 3D DEPTH CAMERA |
spellingShingle |
AN AUTOMATIC DETECTION METHOD FOR ABNORMAL LAYING HEN ACTIVITIES USING A 3D DEPTH CAMERA Du,Xiaodong Activity occupation index 3D depth camera MPM laying hens |
title_short |
AN AUTOMATIC DETECTION METHOD FOR ABNORMAL LAYING HEN ACTIVITIES USING A 3D DEPTH CAMERA |
title_full |
AN AUTOMATIC DETECTION METHOD FOR ABNORMAL LAYING HEN ACTIVITIES USING A 3D DEPTH CAMERA |
title_fullStr |
AN AUTOMATIC DETECTION METHOD FOR ABNORMAL LAYING HEN ACTIVITIES USING A 3D DEPTH CAMERA |
title_full_unstemmed |
AN AUTOMATIC DETECTION METHOD FOR ABNORMAL LAYING HEN ACTIVITIES USING A 3D DEPTH CAMERA |
title_sort |
AN AUTOMATIC DETECTION METHOD FOR ABNORMAL LAYING HEN ACTIVITIES USING A 3D DEPTH CAMERA |
author |
Du,Xiaodong |
author_facet |
Du,Xiaodong Teng,Guanghui |
author_role |
author |
author2 |
Teng,Guanghui |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Du,Xiaodong Teng,Guanghui |
dc.subject.por.fl_str_mv |
Activity occupation index 3D depth camera MPM laying hens |
topic |
Activity occupation index 3D depth camera MPM laying hens |
description |
ABSTRACT With the increasing scale of farms and the correspondingly higher number of laying hens, it is increasingly difficult for farmers to monitor their animals in a traditional way. Early warning of abnormal animal activities is helpful for farmers’ fast response to the negative impact on animal health, animal welfare and daily management. This study introduces an automatic and non-invasive method for detecting abnormal poultry activities using a 3D depth camera. A typical region including eighteen Hy-line brown laying hens was continuously monitored by a top-view Kinect during 49 continuous days. A mean prediction model (MPM), based on the frame difference algorithm, was built to monitor animal activities and occupation zones. As a result, this method reported abnormal activities with an average accuracy of 84.2% and a rate of misclassifying abnormal events of 15.8% (PFPR). Additionally, it was found that the flock showed a diurnal change pattern in the activity and occupation quantified index. They also presented a similar changing pattern each week. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-05-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162021000300263 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162021000300263 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1809-4430-eng.agric.v41n3p263-270/2021 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia Agrícola |
dc.source.none.fl_str_mv |
Engenharia Agrícola v.41 n.3 2021 reponame:Engenharia Agrícola instname:Associação Brasileira de Engenharia Agrícola (SBEA) instacron:SBEA |
instname_str |
Associação Brasileira de Engenharia Agrícola (SBEA) |
instacron_str |
SBEA |
institution |
SBEA |
reponame_str |
Engenharia Agrícola |
collection |
Engenharia Agrícola |
repository.name.fl_str_mv |
Engenharia Agrícola - Associação Brasileira de Engenharia Agrícola (SBEA) |
repository.mail.fl_str_mv |
revistasbea@sbea.org.br||sbea@sbea.org.br |
_version_ |
1752126274965340160 |