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Journals / Journal of Agricultural Sciences / 2026 / Volume: 31 Issue: 1

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

Havva Eylem Polat(ANKARA ÜNİVERSİTESİ)0000-0002-2159-0666
Dilara Gerdan Koc(ANKARA ÜNİVERSİTESİ)0000-0002-2705-299X
Ömer Ertuğrul(AHİ EVRAN ÜNİVERSİTESİ)0000-0003-0774-1728
Caner Koç(ANKARA ÜNİVERSİTESİ)0000-0002-9096-4254
Kamil Ekinci(ISPARTA UNIVERSITY OF APPLIED SCIENCES)
Pages
—
Publication Date
2025-01-14
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Accurate identification of cattle is essential for monitoring ownership, controlling production supply, preventing disease, and ensuring animal welfare.
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Key Findings & Data:A dataset was generated by capturing images of cattle in their native environment to tackle this issue.
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Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:: Cattle identification · deep learning · face detection

Abstract

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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Cite This Article

Polat, H. E., Koc, D. G., Ertuğrul, Ö., Koç, C., Ekinci, K. (2025). Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning. Journal of Agricultural Sciences, Volume: 31 Issue: 1. https://doi.org/10.15832/ankutbd.1509798

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