Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precision
Artificial intelligence (AI) in animal behavior and welfare research is on the rise. AI can detect behaviors and localize animals in video recordings, thus it is a valuable tool for studying social dynamics. However, maintaining the identity of individuals over time, especially in homogeneous poultr...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Poultry Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0032579124010423 |
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| author | Florencia B. Rossi Nicola Rossi Gabriel Orso Lucas Barberis Raul H. Marin Jackelyn M. Kembro |
| author_facet | Florencia B. Rossi Nicola Rossi Gabriel Orso Lucas Barberis Raul H. Marin Jackelyn M. Kembro |
| author_sort | Florencia B. Rossi |
| collection | DOAJ |
| description | Artificial intelligence (AI) in animal behavior and welfare research is on the rise. AI can detect behaviors and localize animals in video recordings, thus it is a valuable tool for studying social dynamics. However, maintaining the identity of individuals over time, especially in homogeneous poultry flocks, remains challenging for algorithms. We propose using differentially colored “backpack” tags (black, gray, white, orange, red, purple, and green) detectable with computer vision (eg. YOLO) from top-view video recordings of pens. These tags can also accommodate sensors, such as accelerometers. In separate experiments, we aim to: (i) evaluate avian visual perception of the different colored tags; (ii) assess the potential impact of tag colors on social behavior; and (iii) test the ability of the YOLO model to accurately distinguish between different colored tags on Japanese quail in social group settings. First, the reflectance spectra of tags and feathers were measured. An avian visual model was applied to calculate the quantum catches for each spectrum. Green and purple tags showed significant chromatic contrast to the feather. Mostly tags presented greater luminance receptor stimulation than feathers. Birds wearing white, gray, purple, and green tags pecked significantly more at their own tags than those with black (control) tags. Additionally, fewer aggressive interactions were observed in groups with orange tags compared to groups with other colors, except for red. Next, heterogeneous groups of 5 birds with different color tags were videorecorded for 1 h. The precision and accuracy of YOLO to detect each color tag were assessed, yielding values of 95.9% and 97.3%, respectively, with most errors stemming from misclassifications between black and gray tags. Lastly using the YOLO output, we estimated each bird's average social distance, locomotion speed, and the percentage of time spent moving. No behavioral differences associated with tag color were detected. In conclusion, carefully selected colored backpack tags can be identified using AI models and can also hold other sensors, making them powerful tools for behavioral and welfare studies. |
| format | Article |
| id | doaj-art-f8e6a506cdef472886b488fdcd1d137c |
| institution | Kabale University |
| issn | 0032-5791 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Poultry Science |
| spelling | doaj-art-f8e6a506cdef472886b488fdcd1d137c2025-01-22T05:40:10ZengElsevierPoultry Science0032-57912025-01-011041104464Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precisionFlorencia B. Rossi0Nicola Rossi1Gabriel Orso2Lucas Barberis3Raul H. Marin4Jackelyn M. Kembro5Instituto de Investigaciones Biológicas y Tecnológicas (IIByT, CONICET-UNC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Avenida Vélez Sarsfield 1611, Córdoba, Córdoba, Argentina; Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba (UNC), Instituto de Ciencia y Tecnología de los Alimentos (ICTA), Córdoba, Córdoba, ArgentinaUniversidad Nacional de Córdoba, Facultad de Ciencias Exactas Físicas y Naturales, Laboratorio de Biología del Comportamiento, Córdoba, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Diversidad y Ecología Animal (IDEA), Córdoba, ArgentinaInstituto de Investigaciones Biológicas y Tecnológicas (IIByT, CONICET-UNC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Avenida Vélez Sarsfield 1611, Córdoba, Córdoba, Argentina; Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba (UNC), Instituto de Ciencia y Tecnología de los Alimentos (ICTA), Córdoba, Córdoba, ArgentinaFacultad de Matemática, Astronomía Física y Computación, Universidad Nacional de Córdoba, Córdoba, Argentina; Instituto de Física Enrique Gaviola (IFEG, CONICET-UNC), Córdoba, Córdoba, ArgentinaInstituto de Investigaciones Biológicas y Tecnológicas (IIByT, CONICET-UNC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Avenida Vélez Sarsfield 1611, Córdoba, Córdoba, Argentina; Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba (UNC), Instituto de Ciencia y Tecnología de los Alimentos (ICTA), Córdoba, Córdoba, ArgentinaInstituto de Investigaciones Biológicas y Tecnológicas (IIByT, CONICET-UNC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Avenida Vélez Sarsfield 1611, Córdoba, Córdoba, Argentina; Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba (UNC), Instituto de Ciencia y Tecnología de los Alimentos (ICTA), Córdoba, Córdoba, Argentina; Corresponding author at: Instituto de Investigaciones Biológicas y Tecnológicas (IIByT, CONICET-UNC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Avenida Vélez Sarsfield 1611, Córdoba, Córdoba, Argentina.Artificial intelligence (AI) in animal behavior and welfare research is on the rise. AI can detect behaviors and localize animals in video recordings, thus it is a valuable tool for studying social dynamics. However, maintaining the identity of individuals over time, especially in homogeneous poultry flocks, remains challenging for algorithms. We propose using differentially colored “backpack” tags (black, gray, white, orange, red, purple, and green) detectable with computer vision (eg. YOLO) from top-view video recordings of pens. These tags can also accommodate sensors, such as accelerometers. In separate experiments, we aim to: (i) evaluate avian visual perception of the different colored tags; (ii) assess the potential impact of tag colors on social behavior; and (iii) test the ability of the YOLO model to accurately distinguish between different colored tags on Japanese quail in social group settings. First, the reflectance spectra of tags and feathers were measured. An avian visual model was applied to calculate the quantum catches for each spectrum. Green and purple tags showed significant chromatic contrast to the feather. Mostly tags presented greater luminance receptor stimulation than feathers. Birds wearing white, gray, purple, and green tags pecked significantly more at their own tags than those with black (control) tags. Additionally, fewer aggressive interactions were observed in groups with orange tags compared to groups with other colors, except for red. Next, heterogeneous groups of 5 birds with different color tags were videorecorded for 1 h. The precision and accuracy of YOLO to detect each color tag were assessed, yielding values of 95.9% and 97.3%, respectively, with most errors stemming from misclassifications between black and gray tags. Lastly using the YOLO output, we estimated each bird's average social distance, locomotion speed, and the percentage of time spent moving. No behavioral differences associated with tag color were detected. In conclusion, carefully selected colored backpack tags can be identified using AI models and can also hold other sensors, making them powerful tools for behavioral and welfare studies.http://www.sciencedirect.com/science/article/pii/S0032579124010423Computer visionJapanese quailVisual modelPoultryYOLO |
| spellingShingle | Florencia B. Rossi Nicola Rossi Gabriel Orso Lucas Barberis Raul H. Marin Jackelyn M. Kembro Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precision Poultry Science Computer vision Japanese quail Visual model Poultry YOLO |
| title | Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precision |
| title_full | Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precision |
| title_fullStr | Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precision |
| title_full_unstemmed | Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precision |
| title_short | Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precision |
| title_sort | monitoring poultry social dynamics using colored tags avian visual perception behavioral effects and artificial intelligence precision |
| topic | Computer vision Japanese quail Visual model Poultry YOLO |
| url | http://www.sciencedirect.com/science/article/pii/S0032579124010423 |
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