[{
  "type": "article-journal",
  "title": "BIOMETRIC IDENTIFICATION OF WATER BUFFALO USING REAL-TIME FACE RECOGNITION",
  "author": [
    {
      "family": "Tuvay",
      "given": ""
    },
    {
      "family": "Ermetin",
      "given": ""
    },
    {
      "family": "Hayit",
      "given": ""
    }
  ],
  "issued": {
    "date-parts": [[2026]]
  },
  "container-title": "Journal of Animal and Plant Sciences",
  "ISSN": "1018-7081",
  "volume": "36",
  "issue": "6",
  "DOI": "https://doi.org/10.36899/JAPS.2026.6.0130",
  "abstract": "<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>",
  "publisher": "Pakistan Agricultural Scientists Forum",
  "URL": "https://thejaps.org.pk/AbstractView.aspx?mid=2026-JAPS-196"
}]