Comparison of strategies for automatic video-based detection of piling behaviour in laying hens
This study addresses piling behaviour in laying hens, a concern for producers due to welfare issues. Piling is commonly defined as the intense aggregation of birds into dense clusters, typically occurring in large group-housing systems. This behaviour can lead to severe welfare issues including suff...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524003496 |
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| _version_ | 1850041569395081216 |
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| author | Dan Børge Jensen Michael Toscano Esther van der Heide Matias Grønvig Franziska Hakansson |
| author_facet | Dan Børge Jensen Michael Toscano Esther van der Heide Matias Grønvig Franziska Hakansson |
| author_sort | Dan Børge Jensen |
| collection | DOAJ |
| description | This study addresses piling behaviour in laying hens, a concern for producers due to welfare issues. Piling is commonly defined as the intense aggregation of birds into dense clusters, typically occurring in large group-housing systems. This behaviour can lead to severe welfare issues including suffocation for the affected birds, particularly those at the bottom of the pile. This is a preliminary study, using video data from 4 commercial Swiss layer flocks. The long-term goal of our research is to develop a machine learning-based method to automatically detect piling behaviour using a two-step approach: first, a pre-trained convolutional neural network model (VGG-16) is used to extract features from the video data. Second, a secondary model, trained specifically to detect piling, is applied to the extracted features. The specific aim of this preliminary study is to determine which secondary modelling strategy is best suited for the accurate and efficient detection of piling behaviour. Three secondary methods are explored, namely fully connected artificial neural networks (FC-ANN), long-short term memory (LSTM) networks, and convolutional neural networks (CNN). |
| format | Article |
| id | doaj-art-21c9e014780f429ca1606087eb59d88f |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-21c9e014780f429ca1606087eb59d88f2025-08-20T02:55:45ZengElsevierSmart Agricultural Technology2772-37552025-03-011010074510.1016/j.atech.2024.100745Comparison of strategies for automatic video-based detection of piling behaviour in laying hensDan Børge Jensen0Michael Toscano1Esther van der Heide2Matias Grønvig3Franziska Hakansson4Department of Large Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Grønnegårdsvej 2, DK-1870, Frederiksberg C, Denmark; Corresponding author.Center for Proper Housing: Laying hens and Rabbits (ZTHZ), Division of Animal Welfare, VPH Institute, University of Bern, Burgerweg 22, 3052, Zollikofen, SwitzerlandDepartment of Large Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Grønnegårdsvej 2, DK-1870, Frederiksberg C, DenmarkDepartment of Large Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Grønnegårdsvej 2, DK-1870, Frederiksberg C, DenmarkDepartment of Large Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Grønnegårdsvej 2, DK-1870, Frederiksberg C, DenmarkThis study addresses piling behaviour in laying hens, a concern for producers due to welfare issues. Piling is commonly defined as the intense aggregation of birds into dense clusters, typically occurring in large group-housing systems. This behaviour can lead to severe welfare issues including suffocation for the affected birds, particularly those at the bottom of the pile. This is a preliminary study, using video data from 4 commercial Swiss layer flocks. The long-term goal of our research is to develop a machine learning-based method to automatically detect piling behaviour using a two-step approach: first, a pre-trained convolutional neural network model (VGG-16) is used to extract features from the video data. Second, a secondary model, trained specifically to detect piling, is applied to the extracted features. The specific aim of this preliminary study is to determine which secondary modelling strategy is best suited for the accurate and efficient detection of piling behaviour. Three secondary methods are explored, namely fully connected artificial neural networks (FC-ANN), long-short term memory (LSTM) networks, and convolutional neural networks (CNN).http://www.sciencedirect.com/science/article/pii/S2772375524003496Automatic monitoringConvolutional neural networkFully-connected neural networkLong short-term memory networkPiling behaviourLaying hens |
| spellingShingle | Dan Børge Jensen Michael Toscano Esther van der Heide Matias Grønvig Franziska Hakansson Comparison of strategies for automatic video-based detection of piling behaviour in laying hens Smart Agricultural Technology Automatic monitoring Convolutional neural network Fully-connected neural network Long short-term memory network Piling behaviour Laying hens |
| title | Comparison of strategies for automatic video-based detection of piling behaviour in laying hens |
| title_full | Comparison of strategies for automatic video-based detection of piling behaviour in laying hens |
| title_fullStr | Comparison of strategies for automatic video-based detection of piling behaviour in laying hens |
| title_full_unstemmed | Comparison of strategies for automatic video-based detection of piling behaviour in laying hens |
| title_short | Comparison of strategies for automatic video-based detection of piling behaviour in laying hens |
| title_sort | comparison of strategies for automatic video based detection of piling behaviour in laying hens |
| topic | Automatic monitoring Convolutional neural network Fully-connected neural network Long short-term memory network Piling behaviour Laying hens |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524003496 |
| work_keys_str_mv | AT danbørgejensen comparisonofstrategiesforautomaticvideobaseddetectionofpilingbehaviourinlayinghens AT michaeltoscano comparisonofstrategiesforautomaticvideobaseddetectionofpilingbehaviourinlayinghens AT esthervanderheide comparisonofstrategiesforautomaticvideobaseddetectionofpilingbehaviourinlayinghens AT matiasgrønvig comparisonofstrategiesforautomaticvideobaseddetectionofpilingbehaviourinlayinghens AT franziskahakansson comparisonofstrategiesforautomaticvideobaseddetectionofpilingbehaviourinlayinghens |