Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models
Mating behaviors are crucial for bird welfare, reproduction, and productivity in breeding flocks. During mating, a rooster mounts a hen, which may result in the hen overlapping or disappearing from top-view of a vision system. The objective of this research was to develop Deep learning models (DLM)...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
|
| Series: | Poultry Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0032579125003657 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849421785377800192 |
|---|---|
| author | Venkat U.C. Bodempudi Guoming Li J. Hunter Mason Jeanna L. Wilson Tianming Liu Khaled M. Rasheed |
| author_facet | Venkat U.C. Bodempudi Guoming Li J. Hunter Mason Jeanna L. Wilson Tianming Liu Khaled M. Rasheed |
| author_sort | Venkat U.C. Bodempudi |
| collection | DOAJ |
| description | Mating behaviors are crucial for bird welfare, reproduction, and productivity in breeding flocks. During mating, a rooster mounts a hen, which may result in the hen overlapping or disappearing from top-view of a vision system. The objective of this research was to develop Deep learning models (DLM) to identify mating behavior based on bird count changes and bio-characteristics of mating. Twenty broiler breeder hens and 2-3 roosters (56 weeks) of the Ross 708 breed were monitored in four experimental pens. The DLM framework included a bird detection model, data filtering algorithms based on mating duration, and logic frameworks for mating identification based on bird count changes. Pretrained models of object detection (You Only Look Once Version 7 and 8, YOLOv7 and YOLOv8), tracking (YOLOv7 or YOLOv8 with Deep Simple Online Real-time Tracking (SORT), StrongSORT, and ByteTrack), and segmentation (Segment Anything Model2 (SAM2), YOLOv8-segmentation, Track Anything) were comparatively evaluated for bird detection, and YOLOv8l object detection model was selected due to balanced performance in processing speed (8 seconds per frame) and accuracy (75 % Mean Average Precision (mAP)). With custom training, the best performance of detecting broiler breeders via YOLOv8l was over 0.939 precision, recall, mAP50, mAP95, and F1 score for training and 0.95 positive and negative predicted values for testing. After comparing 24 scenarios of mating duration and 32 scenarios of time interval, a mating duration of 3-9 seconds and the time intervals of T-3 to T+12 seconds based on manual observation were incorporated into the framework to filter out unnecessary data and retain keyframes for further processing, significantly reducing the processing speed by a factor of 10. The optimized framework was effectively able to detect the birds and identify the mating behavior with 0.92 accuracy compared to other YOLO detection plus logic frameworks. Mating event identification via the developed DLM framework fluctuated among different time of a day and bird ages due to bird overlapping, gathering densities, and occlusions. By automating this process, breeders can efficiently monitor and analyze mating behaviors, facilitating timely interventions and adjustments in housing and management practices to optimize broiler breeder fertility, genetics, and overall productivity. |
| format | Article |
| id | doaj-art-d83d52ad8031433286132638d8ecc916 |
| institution | Kabale University |
| issn | 0032-5791 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Poultry Science |
| spelling | doaj-art-d83d52ad8031433286132638d8ecc9162025-08-20T03:31:23ZengElsevierPoultry Science0032-57912025-07-01104710512610.1016/j.psj.2025.105126Identifying mating events of group-housed broiler breeders via bio-inspired deep learning modelsVenkat U.C. Bodempudi0Guoming Li1J. Hunter Mason2Jeanna L. Wilson3Tianming Liu4Khaled M. Rasheed5Department of Poultry Science, University of Georgia, Athens, GA 30602, USA; Institute for Artificial Intelligence, University of Georgia, Athens, GA 30602, USADepartment of Poultry Science, University of Georgia, Athens, GA 30602, USA; Institute for Artificial Intelligence, University of Georgia, Athens, GA 30602, USA; Corresponding author.Department of Poultry Science, University of Georgia, Athens, GA 30602, USADepartment of Poultry Science, University of Georgia, Athens, GA 30602, USASchool of Computing, University of Georgia, Athens, GA 30602, USAInstitute for Artificial Intelligence, University of Georgia, Athens, GA 30602, USAMating behaviors are crucial for bird welfare, reproduction, and productivity in breeding flocks. During mating, a rooster mounts a hen, which may result in the hen overlapping or disappearing from top-view of a vision system. The objective of this research was to develop Deep learning models (DLM) to identify mating behavior based on bird count changes and bio-characteristics of mating. Twenty broiler breeder hens and 2-3 roosters (56 weeks) of the Ross 708 breed were monitored in four experimental pens. The DLM framework included a bird detection model, data filtering algorithms based on mating duration, and logic frameworks for mating identification based on bird count changes. Pretrained models of object detection (You Only Look Once Version 7 and 8, YOLOv7 and YOLOv8), tracking (YOLOv7 or YOLOv8 with Deep Simple Online Real-time Tracking (SORT), StrongSORT, and ByteTrack), and segmentation (Segment Anything Model2 (SAM2), YOLOv8-segmentation, Track Anything) were comparatively evaluated for bird detection, and YOLOv8l object detection model was selected due to balanced performance in processing speed (8 seconds per frame) and accuracy (75 % Mean Average Precision (mAP)). With custom training, the best performance of detecting broiler breeders via YOLOv8l was over 0.939 precision, recall, mAP50, mAP95, and F1 score for training and 0.95 positive and negative predicted values for testing. After comparing 24 scenarios of mating duration and 32 scenarios of time interval, a mating duration of 3-9 seconds and the time intervals of T-3 to T+12 seconds based on manual observation were incorporated into the framework to filter out unnecessary data and retain keyframes for further processing, significantly reducing the processing speed by a factor of 10. The optimized framework was effectively able to detect the birds and identify the mating behavior with 0.92 accuracy compared to other YOLO detection plus logic frameworks. Mating event identification via the developed DLM framework fluctuated among different time of a day and bird ages due to bird overlapping, gathering densities, and occlusions. By automating this process, breeders can efficiently monitor and analyze mating behaviors, facilitating timely interventions and adjustments in housing and management practices to optimize broiler breeder fertility, genetics, and overall productivity.http://www.sciencedirect.com/science/article/pii/S0032579125003657PoultryPrecision agricultureArtificial intelligenceComputer visionBehavior detection |
| spellingShingle | Venkat U.C. Bodempudi Guoming Li J. Hunter Mason Jeanna L. Wilson Tianming Liu Khaled M. Rasheed Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models Poultry Science Poultry Precision agriculture Artificial intelligence Computer vision Behavior detection |
| title | Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models |
| title_full | Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models |
| title_fullStr | Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models |
| title_full_unstemmed | Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models |
| title_short | Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models |
| title_sort | identifying mating events of group housed broiler breeders via bio inspired deep learning models |
| topic | Poultry Precision agriculture Artificial intelligence Computer vision Behavior detection |
| url | http://www.sciencedirect.com/science/article/pii/S0032579125003657 |
| work_keys_str_mv | AT venkatucbodempudi identifyingmatingeventsofgrouphousedbroilerbreedersviabioinspireddeeplearningmodels AT guomingli identifyingmatingeventsofgrouphousedbroilerbreedersviabioinspireddeeplearningmodels AT jhuntermason identifyingmatingeventsofgrouphousedbroilerbreedersviabioinspireddeeplearningmodels AT jeannalwilson identifyingmatingeventsofgrouphousedbroilerbreedersviabioinspireddeeplearningmodels AT tianmingliu identifyingmatingeventsofgrouphousedbroilerbreedersviabioinspireddeeplearningmodels AT khaledmrasheed identifyingmatingeventsofgrouphousedbroilerbreedersviabioinspireddeeplearningmodels |