Deep learning in poultry farming: comparative analysis of Yolov8, Yolov9, Yolov10, and Yolov11 for dead chickens detection

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/60076658:12220/25:43909785

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep learning in poultry farming: comparative analysis of Yolov8, Yolov9, Yolov10, and Yolov11 for dead chickens detection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Deep learning in poultry farming: comparative analysis of Yolov8, Yolov9, Yolov10, and Yolov11 for dead chickens detection

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40201 - Animal and dairy science; (Animal biotechnology to be 4.4)

Projekt

<a href="/cs/project/TM04000023" target="_blank" >TM04000023: Výzkum a vývoj klíčových technologií pro chovy drůbeže – systém pro monitorování zvířat a prostředí a inteligentní management chovu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Poultry Science

ISSN

0032-5791

e-ISSN

1525-3171

Svazek periodika

104

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

"1–8"

Kód UT WoS článku

001518193000001

EID výsledku v databázi Scopus

2-s2.0-105008566034