Performance of machine learning methods for cattle identification and recognition from retinal images
Yazarlar (8)
Doç. Dr. Pınar Cihan Tekirdağ Namık Kemal Üniversitesi, Türkiye
Doç. Dr. Ahmet Saygılı Tekirdağ Namık Kemal Üniversitesi, Türkiye
Muhammed Akyüzlü
Arş. Gör. Nihat Eren Özmen Tekirdağ Namık Kemal Üniversitesi, Türkiye
Prof. Dr. Celal Şahin ERMUTLU Kafkas Üniversitesi, Türkiye
Doç. Dr. Uğur AYDIN Kafkas Üniversitesi, Türkiye
Dr. Öğr. Üyesi Alican YILMAZ Kafkas Üniversitesi, Türkiye
Prof. Dr. Özgür AKSOY Kafkas Üniversitesi, Türkiye
Makale Türü Açık Erişim Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
Dergi Adı Applied Intelligence (Q2)
Dergi ISSN 0924-669X Dergi Bilgileri (2025)
Dergi Tarandığı Indeksler SCI-Expanded
Makale Dili Türkçe Basım Tarihi 01-2025
Cilt / Sayı / Sayfa 55 / 0 / – DOI 10.1007/s10489-025-06398-1
Makale Linki https://doi.org/10.1007/s10489-025-06398-1
UAK Araştırma Alanları
Veteriner Cerrahisi
Özet
Abstract
Animal identification is a critical issue in terms of security, traceability, and animal health, especially in large-scale livestock enterprises. Traditional methods (such as ear tags and branding) both negatively affect animal welfare and may lead to security vulnerabilities. This study aims to develop a biometric system based on retinal vascular patterns for the identification and recognition of cattle. This system aims to provide a safer and animal welfare-friendly alternative by using image processing techniques instead of traditional device-based methods. In the study, preprocessing, segmentation, feature extraction, and performance evaluation steps were applied for the biometric identification and recognition process using retinal images taken from both eyes. Techniques such as green channel extraction, contrast-limited adaptive histogram equalization, morphological operations, noise filtering, and threshold determination were used in the preprocessing stage. Fuzzy C-means, K-means, and Level-set methods were applied for segmentation, and feature extraction was performed using SIFT, SURF, BRISK, FAST, and HARRIS methods. At the end of the study, the highest accuracy rate was obtained as 95.6% for identification and 87.9% for recognition. In addition, the obtained dataset was shared publicly, thus creating a reusable resource that researchers from different disciplines can use. It was concluded that this study made a significant contribution to the field of biometric-based animal identification and recognition and offered a practically usable solution in terms of animal welfare and safety.
Anahtar Kelimeler
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Performance of machine learning methods for cattle identification and recognition from retinal images

Paylaş

İletişim
  • Kafkas Üniversitesi Rektörlüğü (36100) Merkez / KARS
  • unis@kafkas.edu.tr 4742251150 (474)225-1160
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