Czech J. Anim. Sci., 2025, 70(9):383-396 | DOI: 10.17221/66/2025-CJAS

Computer vision-based approaches to cattle identification: A comparative evaluation of body texture, QR code, and numerical labellingOriginal Paper

Roman Bumbálek ORCID...¹, Jean de Dieu Marcel Ufitikirezi ORCID...¹, Tomáš Zoubek ORCID...¹, Sandra Nicole Umurungi ORCID...¹, Radim Stehlík ORCID...¹, Zbyněk Havelka ORCID...¹, Radim Kuneš ORCID...¹, Petr Bartoš ORCID...^1,2

¹ Department of Technology and Cybernetics, Faculty of Agriculture and Technology, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic

² Department of Applied Physics and Technology, Faculty of Education, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic

Cattle identification systems are advancing to meet the growing demands of precision livestock management, traceability, and ethical animal treatment. This study investigates three methods: body texture recognition, QR code collars, and numerical labelling, implemented using the YOLOv8 convolutional neural network. Each method was evaluated in terms of accuracy, scalability, adaptability to dynamic herd changes, and operational efficiency under various environmental conditions. Body texture recognition, while leveraging unique natural patterns and achieving a mean Average Precision (mAP50–95) of 0.78 proved limited by its reliance on frequent dataset retraining to accommodate changes in herd composition and susceptibility to misidentification in larger herds. QR code collars demonstrated adaptability in dynamic herds by enabling pre-trained convolutional neural networks to assign reserved codes to new animals without retraining, while removing animals involves simply deleting their codes from the system. This approach also achieved an mAP50–95 of 0.71, which was lower than the body texture-based approach, but offered greater flexibility in herd management. Despite this adaptability, this method demonstrated significant challenges in real-world environments. Occlusion caused by feeders, barriers, or animal movements, along with low-resolution imaging and poor lighting conditions, can compromise detection accuracy, particularly in larger herds with obstructive barn layouts. The numerical labelling method emerged as the most effective solution to dynamic cattle identification, achieving the highest mAP50–95 of 0.84. It provided a scalable and highly accurate approach that integrates seamlessly with automated systems. Unlike traditional body marking techniques such as ear notching and branding, numerical labelling is less invasive, painless, and highly scalable, aligning with ethical livestock management practices while maintaining consistent accuracy across diverse environmental conditions.

Keywords: animal welfare; convolutional neural networks; herd monitoring; livestock biometrics; object detection; precision livestock farming

Received: May 10, 2025; Accepted: September 10, 2025; Prepublished online: September 26, 2025; Published: September 29, 2025  Show citation