Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning

Caner Koç; Kamil Ekinci; Ömer Ertuğrul; Dilara Gerdan Koc; Havva Eylem Polat

Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning

2025

Caner Koç | Kamil Ekinci | Ömer Ertuğrul | Dilara Gerdan Koc | Havva Eylem Polat

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.

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