Computer vision in precision livestock farming: benchmarking YOLOv9, YOLOv10, YOLOv11, and YOLOv12 for individual cattle identification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12410%2F25%3A43909784" target="_blank" >RIV/60076658:12410/25:43909784 - isvavai.cz</a>
Alternative codes found
RIV/60076658:12220/25:43909784
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2772375525004393?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2772375525004393?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.atech.2025.101208" target="_blank" >10.1016/j.atech.2025.101208</a>
Alternative languages
Result language
angličtina
Original language name
Computer vision in precision livestock farming: benchmarking YOLOv9, YOLOv10, YOLOv11, and YOLOv12 for individual cattle identification
Original language description
Individual animal identification is fundamental to effective livestock traceability and precision management. This study evaluates the performance of four recent object detection models (YOLOv9m, YOLOv10m, YOLOv11m, and YOLOv12m) for automated cattle identification using a numerical labelling approach in real barn environments. A custom dataset comprising 91,694 annotated frames was collected from a multi-camera surveillance system deployed in a barn area housing dairy cows. The cameras were strategically positioned to provide overlapping coverage and to capture animals under varied barn lighting conditions and crowded environments. Each model was trained and assessed using standard performance metrics, including mean average precision (mAP) at Intersection over Union (IoU) thresholds of 0.50 and 0.50-0.95, as well as precision, recall, inference speed, and model size. Among the models evaluated, YOLOv12m achieved the highest detection accuracy (mAP50 = 0.947; mAP50-95 = 0.911), indicating strong capability in distinguishing individual cattle based on numerical markings even under complex environments. YOLOv11m offered a competitive balance between detection accuracy and computational efficiency, making it suitable for real-time applications. The study also compared model performance with findings from earlier YOLO-based approaches and highlighted significant improvements in robustness and deployment readiness offered by newer versions. These results demonstrate that recent YOLO models are well-suited for individual cattle identification in practical farm environments. The findings provide useful guidance for selecting models based on operational requirements such as accuracy, processing speed, and device constraints, contributing to the advancement of computer vision applications in precision livestock farming.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
40201 - Animal and dairy science; (Animal biotechnology to be 4.4)
Result continuities
Project
<a href="/en/project/FW03010447" target="_blank" >FW03010447: Development of an intelligent system for increasing the performance of dairy cattle using artificial intelligence methods</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
Smart Agricultural Technology
ISSN
2772-3755
e-ISSN
2772-3755
Volume of the periodical
12
Issue of the periodical within the volume
July
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
11
Pages from-to
"1–11"
UT code for WoS article
001555322700001
EID of the result in the Scopus database
2-s2.0-105010871140