BIOMETRIC IDENTIFICATION OF WATER BUFFALO USING REAL-TIME FACE RECOGNITION
JOURNAL OF ANIMAL AND PLANT SCIENCES, cilt.36, sa.6, ss.1-16, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 36 Sayı: 6
- Basım Tarihi: 2026
- Doi Numarası: 10.36899/japs.2026.6.0130
- Dergi Adı: JOURNAL OF ANIMAL AND PLANT SCIENCES
- Derginin Tarandığı İndeksler: Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO), Business Source Ultimate (EBSCO), Scopus, Science Citation Index Expanded (SCI-EXPANDED), CAB Abstracts
- Sayfa Sayıları: ss.1-16
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Yozgat Bozok Üniversitesi Adresli: Evet
Özet
Face recognition is increasingly used for biometric identification in both humans and animals, offering a non-invasive option for managing hard-to-handle species such as water buffalo. We present a real-time face recognition system based on YOLOv5 to accurately identify individual buffalo in a livestock setting. Our goal is to support precision livestock farming with an efficient and scalable monitoring solution. We also introduce Buffalo-22, a dataset of 4000 augmented face images from eight water buffalo collected at a breeding farm in Yozgat, Turkey. We compare our system with a traditional approach based on Local Binary Patterns in the HSV color space (LBP-HSV) to highlight the advantages of deep learning in challenging agricultural environments. The proposed YOLOv5 model achieved 99.3% average precision at IoU 0.5 (mAP@0.5), while the LBP-HSV model reached 87.8% test accuracy. These results show that convolutional neural networks can deliver accurate, real-time face recognition for water buffalo. The reported performance indicates that the proposed system is a practical tool for automatic and reliable identification on livestock farm