Deep learning in poultry farming: comparative analysis of Yolov8, Yolov9, Yolov10, and Yolov11 for dead chickens detection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12410%2F25%3A43909785" target="_blank" >RIV/60076658:12410/25:43909785 - isvavai.cz</a>
Alternative codes found
RIV/60076658:12220/25:43909785
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0032579125006844?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0032579125006844?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.psj.2025.105440" target="_blank" >10.1016/j.psj.2025.105440</a>
Alternative languages
Result language
angličtina
Original language name
Deep learning in poultry farming: comparative analysis of Yolov8, Yolov9, Yolov10, and Yolov11 for dead chickens detection
Original language description
Automated detection of dead chickens is essential for enhancing biosecurity, animal welfare, and operational efficiency in poultry farms. This study evaluates the performance of YOLOv8n, YOLOv9c, YOLOv10n, and YOLOv11n for detecting dead chickens in cage-free poultry farms. A synthetic dataset of 3413 images was created by compositing manually annotated images of dead and healthy chickens into realistic stall backgrounds to simulate real farm conditions. The models were assessed using standard object detection metrics (precision, recall, and mean average precision (mAP) at IoU thresholds of 0.5 and 0.5-0.95) alongside computational efficiency indicators including inference speed, frames per second (FPS), model size, and training time. YOLOv9c achieved the highest detection accuracy (mAP@50 = 0.983, mAP@50-95 = 0.93), making it the most reliable for minimising false positives and missed detections. YOLOv11n delivered the fastest inference speed (2.8 ms/frame, similar to 357 FPS), making it more suitable for real-time applications. These results underscore the importance of selecting a YOLO model based on farm-specific operational constraints. YOLOv9c is recommended for accuracy-critical tasks, YOLOv11n for real-time monitoring, and YOLOv8n or YOLOv10n for resource-limited deployments. Comparative analysis with earlier YOLO models (YOLOv3-YOLOv7) shows that newer versions improve both detection reliability and processing speed. This work contributes a performance benchmark to guide AI-based poultry monitoring and highlights future directions, including real-world deployment and validation under live farm conditions.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
40201 - Animal and dairy science; (Animal biotechnology to be 4.4)
Result continuities
Project
<a href="/en/project/TM04000023" target="_blank" >TM04000023: Joint research and development of key technology for broiler breeding environment monitoring and intelligent control</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Poultry Science
ISSN
0032-5791
e-ISSN
1525-3171
Volume of the periodical
104
Issue of the periodical within the volume
9
Country of publishing house
GB - UNITED KINGDOM
Number of pages
8
Pages from-to
"1–8"
UT code for WoS article
001518193000001
EID of the result in the Scopus database
2-s2.0-105008566034