Short-Term Longitudinal Biometric Identification of Cattle Using Planum Nasolabiale Images and a Two-Stage Deep Learning Framework
Veterinary Sciences, cilt.13, sa.9, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 13 Sayı: 9
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/vetsci13090924
- Dergi Adı: Veterinary Sciences
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, EMBASE, Directory of Open Access Journals, Natural Science Collection (ProQuest), Biological Science Database (ProQuest)
- Anahtar Kelimeler: biometric identification, cattle identification, deep learning, EfficientNet, longitudinal validation, muzzle pattern, planum nasolabiale, precision livestock farming, YOLOv12
- Erciyes Üniversitesi Adresli: Evet
Özet
Individual cattle identification is essential for traceability and precision livestock management, but conventional identifiers may be lost or damaged. This study evaluated a non-invasive biometric framework based on PN images. A two-stage pipeline combined a YOLOv12n detector for muzzle localization with an ImageNet-pretrained EfficientNetB0 classifier for individual identification. Forty-eight cattle (32 adult cows and 16 calves) were photographed on 31 January 2025 (T0) and again on 11 June 2025 (T1), a 131-day interval. Four images per animal were obtained at each session, producing 384 longitudinal images. The detector-development dataset comprised 565 original images from cattle sampled independently at a slaughterhouse and 210 public Zenodo images, with no animal- or image-level overlap with the longitudinal dataset. The 775 detector images were randomly partitioned at image level into training (n = 543), validation (n = 116), and test (n = 116) subsets before augmentation; online augmentation was applied only to the training subset. Muzzle detection achieved precision of 0.890, recall of 0.978, and mAP@0.5 of 0.982. The muzzle was localized in all validation and test images, with a reported mean intersection over union of 0.96. All T1 images were excluded from model development and used as an independent closed-set temporal test, yielding 96.3% overall re-identification accuracy. End-to-end inference required 8.3 ms per image on an NVIDIA L4 GPU. The results support short-term retention of discriminative PN characteristics, including in a cohort containing growing calves, and indicate that PN imaging may serve as a complementary cattle-identification tool.