| Yazarlar (5) |
Havva Eylem Polat
Türkiye |
|
Dilara Gerdan Koç
|
Doç. Dr. Ömer ERTUĞRUL
Kırşehir Ahi Evran Üniversitesi, Türkiye |
|
Caner Koç
Türkiye |
|
Kamil Ekinci
Türkiye |
| 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 of 15 000 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 |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | ESCI dergilerinde yayımlanan tam makale |
| Dergi Adı | Journal of Agricultural Sciences |
| Dergi ISSN | 1391-9318 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2025 |
| Cilt No | 31 |
| Sayı | 1 |
| Sayfalar | 137 / 150 |
| Doi Numarası | 10.15832/ankutbd.1509798 |
| Makale Linki | https://doi.org/10.15832/ankutbd.1509798 |
| Atıf Sayıları | |
| WoS | 3 |
| Google Scholar | 5 |
