Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning
Yazarlar (5)
Prof. Dr. Havva Eylem Polat Ankara Üniversitesi, Türkiye
Dilara Gerdan Koç
Ankara University, Türkiye
Ankara University, Türkiye
Doç. Dr. Ömer ERTUĞRUL Kırşehir Ahi Evran Üniversitesi, Türkiye
Prof. Dr. Caner Koç Ankara University, Türkiye
Prof. Dr. Kamil Ekinci Isparta Uygulamalı Bilimler Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Tarım Bilimleri Dergisi (Q2) | ||
| Dergi ISSN | 1300-7580 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2025 |
| Cilt / Sayı / Sayfa | 31 / 1 / 137–150 | DOI | 10.15832/ankutbd.1509798 |
| Makale Linki | https://doi.org/10.15832/ankutbd.1509798 | ||
| UAK Araştırma Alanları |
Tarımsal Otomasyon
Hassas/Akıllı Tarım Teknolojileri
Hayvansal Üretimde Mekanizasyon
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| Özet |
| Accurate identification of cattle is essential for monitoring ownership, controlling production supply, preventing disease, and ensuring animal welfare. Despite the widespread use of ear tag-based techniques in livestock farm management, large-scale farms encounter challenges in identifying individual cattle. The process of identifying individual animals can be hindered by ear tags that fall off, and the ability to identify them over a long period of time becomes impossible when tags are missing. A dataset was generated by capturing images of cattle in their native environment to tackle this issue. The dataset was divided into three segments: training, validation, and testing. The dataset consisted of15 000 records, each pertaining to a distinct bovine specimen from a total of 30 different cattle. To identify specific cattle faces in this study, deep learning algorithms such as InceptionResNetV2, MobileNetV2, DenseNet201, Xception, and NasNetLarge were utilized. The DenseNet201 algorithm attained a peak test accuracy of 99.53% and a validation accuracy of 99.83%. Additionally, this study introduces a novel approach that integrates advanced image processing techniques with deep learning, providing a robust framework that can potentially be applied to other domains of animal identification, thus enhancing overall farm management and biosecurity. |
| Anahtar Kelimeler |
| Cattle identification | Deep learning | Face detection | Smart farming |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 7 |
| Scopus | 9 |
| Google Scholar | 13 |