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

Roman Bumbálek, Jean de Dieu Marcel Ufitikirezi, Tomáš Zoubek, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš and Petr Bartoš
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Roman Bumbálek: Department of Technology and Cybernetics, Faculty of Agriculture and Technology, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic
Jean de Dieu Marcel Ufitikirezi: Department of Technology and Cybernetics, Faculty of Agriculture and Technology, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic
Tomáš Zoubek: Department of Technology and Cybernetics, Faculty of Agriculture and Technology, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic
Sandra Nicole Umurungi: Department of Technology and Cybernetics, Faculty of Agriculture and Technology, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic
Radim Stehlík: Department of Technology and Cybernetics, Faculty of Agriculture and Technology, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic
Zbyněk Havelka: Department of Technology and Cybernetics, Faculty of Agriculture and Technology, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic
Radim Kuneš: Department of Technology and Cybernetics, Faculty of Agriculture and Technology, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic
Petr Bartoš: Department of Technology and Cybernetics, Faculty of Agriculture and Technology, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic

Czech Journal of Animal Science, 2025, vol. 70, issue 9, 383-396

Abstract: 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 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlcjs:v:70:y:2025:i:9:id:66-2025-cjas

DOI: 10.17221/66/2025-CJAS

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