Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning
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 individu...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Faculty of Agriculture, Ankara University
2025-01-01
|
| Series: | Journal of Agricultural Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/en/download/article-file/4042332 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832576146302042112 |
|---|---|
| author | Caner Koç Kamil Ekinci Ömer Ertuğrul Dilara Gerdan Koc Havva Eylem Polat |
| author_facet | Caner Koç Kamil Ekinci Ömer Ertuğrul Dilara Gerdan Koc Havva Eylem Polat |
| author_sort | Caner Koç |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9f217ae43d0640f6a72ce37f3528cc0e |
| institution | Kabale University |
| issn | 1300-7580 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Faculty of Agriculture, Ankara University |
| record_format | Article |
| series | Journal of Agricultural Sciences |
| spelling | doaj-art-9f217ae43d0640f6a72ce37f3528cc0e2025-01-31T10:57:51ZengFaculty of Agriculture, Ankara UniversityJournal of Agricultural Sciences1300-75802025-01-0131113715010.15832/ankutbd.150979845Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer LearningCaner Koç0https://orcid.org/0000-0002-9096-4254Kamil Ekinci1Ömer Ertuğrul2https://orcid.org/0000-0003-0774-1728Dilara Gerdan Koc3https://orcid.org/0000-0002-2705-299XHavva Eylem Polat4https://orcid.org/0000-0002-2159-0666ANKARA ÜNİVERSİTESİISPARTA UNIVERSITY OF APPLIED SCIENCESAHİ EVRAN ÜNİVERSİTESİANKARA ÜNİVERSİTESİANKARA ÜNİVERSİTESİAccurate 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.https://dergipark.org.tr/en/download/article-file/4042332: cattle identificationdeep learningface detectionsmart farming |
| spellingShingle | 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 Journal of Agricultural Sciences : cattle identification deep learning face detection smart farming |
| title | Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning |
| title_full | Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning |
| title_fullStr | Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning |
| title_full_unstemmed | Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning |
| title_short | Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning |
| title_sort | deep learning based individual cattle face recognition using data augmentation and transfer learning |
| topic | : cattle identification deep learning face detection smart farming |
| url | https://dergipark.org.tr/en/download/article-file/4042332 |
| work_keys_str_mv | AT canerkoc deeplearningbasedindividualcattlefacerecognitionusingdataaugmentationandtransferlearning AT kamilekinci deeplearningbasedindividualcattlefacerecognitionusingdataaugmentationandtransferlearning AT omerertugrul deeplearningbasedindividualcattlefacerecognitionusingdataaugmentationandtransferlearning AT dilaragerdankoc deeplearningbasedindividualcattlefacerecognitionusingdataaugmentationandtransferlearning AT havvaeylempolat deeplearningbasedindividualcattlefacerecognitionusingdataaugmentationandtransferlearning |