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

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

Makale detayları

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

Yazarlar:

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

Yayıncı:

Ankara University

Ana Başlıklar:

Precision Agriculture Technologies | Animal Welfare

Dergi:

Journal of Agricultural Sciences Vol.31 Issue 1, pp.137-150

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001338865

pISSN:

1300-7580

Anahtar kelimeler:

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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

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

Yayıncı:

Ankara University

Ana Başlıklar:

Precision Agriculture Technologies | Animal Welfare

Dergi:

Journal of Agricultural Sciences Vol.31 Issue 1, pp.137-150

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001338865

pISSN:

1300-7580

: Cattle identification

deep learning

face detection

smart farming

Sayı

Makale detayları

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

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

Yayıncı: Ankara University

Ana Başlıklar: Precision Agriculture Technologies | Animal Welfare

Dergi: Journal of Agricultural Sciences Vol.31 Issue 1, pp.137-150

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001338865

pISSN: 1300-7580

: Cattle identification

deep learning

face detection

smart farming

Sayı

Yazarlar:

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

Yayıncı:

Ankara University

Ana Başlıklar:

Precision Agriculture Technologies | Animal Welfare

Dergi:

Journal of Agricultural Sciences Vol.31 Issue 1, pp.137-150

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001338865

pISSN:

1300-7580