Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification
The chicken is the world’s most farmed animal. In this work, we introduce the Chicks4FreeID dataset, the first publicly available dataset focused on the reidentification of individual chickens. We begin by providing a comprehensive overview of the existing animal reidentification datasets. Next, we...
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MDPI AG
2024-12-01
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| Series: | Animals |
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| author | Daria Kern Tobias Schiele Ulrich Klauck Winfred Ingabire |
| author_facet | Daria Kern Tobias Schiele Ulrich Klauck Winfred Ingabire |
| author_sort | Daria Kern |
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| description | The chicken is the world’s most farmed animal. In this work, we introduce the Chicks4FreeID dataset, the first publicly available dataset focused on the reidentification of individual chickens. We begin by providing a comprehensive overview of the existing animal reidentification datasets. Next, we conduct closed-set reidentification experiments on the introduced dataset, using transformer-based feature extractors in combination with two different classifiers. We evaluate performance across domain transfer, supervised, and one-shot learning scenarios. The results demonstrate that transfer learning is particularly effective with limited data, and training from scratch is not necessarily advantageous even when sufficient data are available. Among the evaluated models, the vision transformer paired with a linear classifier achieves the highest performance, with a mean average precision of 97.0%, a top-1 accuracy of 95.1%, and a top-5 accuracy of 100.0%. Our evaluation suggests that the vision transformer architecture produces higher-quality embedding clusters than the Swin transformer architecture. All data and code are publicly shared under a CC BY 4.0 license. |
| format | Article |
| id | doaj-art-d635d1d6de374eb7b538aeb14faa0f01 |
| institution | Kabale University |
| issn | 2076-2615 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj-art-d635d1d6de374eb7b538aeb14faa0f012025-01-10T13:13:45ZengMDPI AGAnimals2076-26152024-12-01151110.3390/ani15010001Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive ReidentificationDaria Kern0Tobias Schiele1Ulrich Klauck2Winfred Ingabire3Faculty Electronics & Computer Science, Aalen University, 73430 Aalen, GermanyFaculty Electronics & Computer Science, Aalen University, 73430 Aalen, GermanyFaculty Electronics & Computer Science, Aalen University, 73430 Aalen, GermanySchool of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UKThe chicken is the world’s most farmed animal. In this work, we introduce the Chicks4FreeID dataset, the first publicly available dataset focused on the reidentification of individual chickens. We begin by providing a comprehensive overview of the existing animal reidentification datasets. Next, we conduct closed-set reidentification experiments on the introduced dataset, using transformer-based feature extractors in combination with two different classifiers. We evaluate performance across domain transfer, supervised, and one-shot learning scenarios. The results demonstrate that transfer learning is particularly effective with limited data, and training from scratch is not necessarily advantageous even when sufficient data are available. Among the evaluated models, the vision transformer paired with a linear classifier achieves the highest performance, with a mean average precision of 97.0%, a top-1 accuracy of 95.1%, and a top-5 accuracy of 100.0%. Our evaluation suggests that the vision transformer architecture produces higher-quality embedding clusters than the Swin transformer architecture. All data and code are publicly shared under a CC BY 4.0 license.https://www.mdpi.com/2076-2615/15/1/1chickenpoultrylivestockre-IDindividual identificationtransformer |
| spellingShingle | Daria Kern Tobias Schiele Ulrich Klauck Winfred Ingabire Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification Animals chicken poultry livestock re-ID individual identification transformer |
| title | Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification |
| title_full | Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification |
| title_fullStr | Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification |
| title_full_unstemmed | Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification |
| title_short | Towards Automated Chicken Monitoring: Dataset and Machine Learning Methods for Visual, Noninvasive Reidentification |
| title_sort | towards automated chicken monitoring dataset and machine learning methods for visual noninvasive reidentification |
| topic | chicken poultry livestock re-ID individual identification transformer |
| url | https://www.mdpi.com/2076-2615/15/1/1 |
| work_keys_str_mv | AT dariakern towardsautomatedchickenmonitoringdatasetandmachinelearningmethodsforvisualnoninvasivereidentification AT tobiasschiele towardsautomatedchickenmonitoringdatasetandmachinelearningmethodsforvisualnoninvasivereidentification AT ulrichklauck towardsautomatedchickenmonitoringdatasetandmachinelearningmethodsforvisualnoninvasivereidentification AT winfredingabire towardsautomatedchickenmonitoringdatasetandmachinelearningmethodsforvisualnoninvasivereidentification |