17 Niyazi Hayrullah Tuvay Orhan Ermetin Tolga Hayit Journal of Animal and Plant Sciences JAPS 20262026 36 6 1018-7081 https://doi.org/10.36899/JAPS.2026.6.0130 <p class="MsoNormal" style="text-align: justify;">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 farms.</p> Biometric identification, face recognition, water buffalo, deep learning, YOLOv5 Pakistan Agricultural Scientists Forum https://thejaps.org.pk/AbstractView.aspx?mid=2026-JAPS-196