Computer vision in precision livestock farming: benchmarking YOLOv9, YOLOv10, YOLOv11, and YOLOv12 for individual cattle identification

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/60076658:12220/25:43909784

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Computer vision in precision livestock farming: benchmarking YOLOv9, YOLOv10, YOLOv11, and YOLOv12 for individual cattle identification

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Computer vision in precision livestock farming: benchmarking YOLOv9, YOLOv10, YOLOv11, and YOLOv12 for individual cattle identification

Popis výsledku anglicky

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.

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/FW03010447" target="_blank" >FW03010447: Vývoj inteligentního systému pro zvyšování užitkovosti dojeného skotu s využitím metod umělé inteligence</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

Smart Agricultural Technology

ISSN

2772-3755

e-ISSN

2772-3755

Svazek periodika

12

Číslo periodika v rámci svazku

July

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

"1–11"

Kód UT WoS článku

001555322700001

EID výsledku v databázi Scopus

2-s2.0-105010871140