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

Yıl 2025, Cilt: 31 Sayı: 1, 137 - 150, 14.01.2025

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

https://doi.org/10.15832/ankutbd.1509798
Cited By: 1

Öz

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

Kaynakça

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  • Andrew W, Greatwood C & Burghardt T (2017). Visual localisation and individual identification of holstein friesian cattle via deep learning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2850-2859. Available from https://openaccess.thecvf.com/content_ICCV_2017_workshops/w41/html/Andrew_Visual_Localisation_and_ICCV_2017_paper.html
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  • Caron M, Touvron H, Misra I, Jégou H, Mairal J, Bojanowski P & Joulin A (2021). Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9650-9660. DOI: https://doi.org/10.48550/arXiv.2104.14294
  • Chen X, Yang T, Mai K, Liu C, Xiong J, Kuan Y & Gao Y (2022). Holstein Cattle Face Re-Identification Unifying Glob-al and Part Feature Deep Network with Attention Mechanism. Animals, 12(8), DOI: https://doi.org/10.3390/ani12081047 DeVries T & Taylor G W (2017). Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552. DOI: https://doi.org/10.48550/arXiv.1708.04552
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Yıl 2025, Cilt: 31 Sayı: 1, 137 - 150, 14.01.2025

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

https://doi.org/10.15832/ankutbd.1509798
Cited By: 1

Öz

Kaynakça

  • Allen A, Golden B, Taylor M, Patterson D, Henriksen D & Skuce R (2008). Evaluation of retinal imaging technology for the biometric identification of bovine animals in Northern Ireland. Livestock science 116(1-3): 42-52. DOI: https://doi.org/10.1016/j.livsci.2007.08.018
  • Andrew W, Greatwood C & Burghardt T (2017). Visual localisation and individual identification of holstein friesian cattle via deep learning. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2850-2859. Available from https://openaccess.thecvf.com/content_ICCV_2017_workshops/w41/html/Andrew_Visual_Localisation_and_ICCV_2017_paper.html
  • Awad A I (2016). From classical methods to animal biometrics: A review on cattle identification and tracking. Computers and Electronics in Agriculture, 123(2016): 423-435, https://doi.org/10.1016/j.compag.2016.03.014
  • Bhatia Y, Bajpayee A, Raghuvanshi D & Mittal H (2019). Image captioning using Google’s inception-resnet-v2 and recurrent neural network. In 2019 Twelfth International Conference on Contemporary Computing (IC3), IEEE Publish-ing, pp. 1-6. DOI: https://doi.org/10.1109/IC3.2019.8844921
  • Cai C & Li J (2013). Cattle face recognition using local binary pattern descriptor. In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, IEEE Publishing pp. 1-4. DOI: https://doi.org/10.1109/APSIPA.2013.6694369
  • Caron M, Touvron H, Misra I, Jégou H, Mairal J, Bojanowski P & Joulin A (2021). Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9650-9660. DOI: https://doi.org/10.48550/arXiv.2104.14294
  • Chen X, Yang T, Mai K, Liu C, Xiong J, Kuan Y & Gao Y (2022). Holstein Cattle Face Re-Identification Unifying Glob-al and Part Feature Deep Network with Attention Mechanism. Animals, 12(8), DOI: https://doi.org/10.3390/ani12081047 DeVries T & Taylor G W (2017). Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552. DOI: https://doi.org/10.48550/arXiv.1708.04552
  • Doersch C, Gupta A & Efros A A (2015). Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1422-1430. Available from https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Doersch_Unsupervised_Visual_Representation_ICCV_2015_paper.html Džermeikaitė K, Bačėninaitė D & Antanaitis R (2023). Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases. Animals, 13(5): 780
  • Fosgate G T, Adesiyun A A & Hird D W (2006). Ear-tag retention and identification methods for extensively managed water buffalo (Bubalus bubalis) in Trinidad. Preventive veterinary medicine, 73(4): 287-296. DOI: https://doi.org/10.1016/j.prevetmed.2005.09.006
  • Gerdan Koc D, Koc C &Vatandas M (2023). Diagnosis of tomato plant diseases using pre-trained architectures and a proposed convolutional neural network model. Journal of Agricultural Sciences (Tarim Bilimleri Dergisi) 29(2): 627-638. doi.org/10.15832/ankutbd.957265
  • Grill J B, Strub F, Altché F, Tallec C, Richemond P, Buchatskaya E & Valko M (2020). Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33, pp: 21271-21284. ISBN: 9781713829546. Available from https://proceedings.neurips.cc/paper/2020/hash/f3ada80d5c4ee70142b17b8192b2958e-Abstract.html
  • Guo S S, Lee K H, Chang L, Tseng C D, Sie S J, Lin G Z & Lee T F (2022). Development of an Automated Body Temperature Detection Platform for Face Recognition in Cattle with YOLO V3-Tiny Deep Learning and Infrared Thermal Imaging. Applied Sciences, 12(8), DOI: https://doi.org/10.3390/app12084036
  • Hansen M F, Smith M L, Smith L N, Salter M G, Baxter E M, Farish M & Grieve B (2018). Towards on-farm pig face recognition using convolutional neural networks. Computers in Industry 98: 145-152
  • Kaixuan Z & Dongjian H (2015). Recognition of individual dairy cattle based on convolutional neural networks. Transactions of the Chinese Society of Agricultural Engineering, 31(5): 181-187. Available from https://www.cabdirect.org/cabdirect/abstract/20153218172
  • Kang X, Zhang X D & Liu G (2020). Accurate detection of lameness in dairy cattle with computer vision: A new and individualized detection strategy based on the analysis of the supporting phase. Journal of dairy science, 103 (11): 10628-10638. DOI: https://doi.org/10.3168/jds.2020-18288
  • Khosla C & Saini B S (2020). Enhancing performance of deep learning models with different data augmentation techniques: A survey. In 2020 International Conference on Intelligent Engineering and Management (ICIEM), IEEE, pp. 79-85, DOI: https://doi.org/10.1109/ICIEM48762.2020.9160048
  • Kumar S, Pandey A, Satwik K S R, Kumar S, Singh S K, Singh A K &Mohan A (2018). Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement, 116 (2018), pp: 1-17, DOI: https://doi.org/10.1016/j.measurement.2017.10.064
  • Kumar S, Singh S K, Dutta T & Gupta H P (2016). A fast cattle recognition system using smart devices. MM ‘16: Proceedings of the 24th ACM international conference on Multimedia, October 2016, pp: 742–743, DOI: https://doi.org/10.1145/2964284.2973829
  • Kumar S, Singh S K, Singh R & Singh A K (2017). Recognition of Cattle Using Face Images. In: Animal Biometrics. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-10-7956-6_3
  • Kusakunniran W & Chaiviroonjaroen T (2018). Automatic cattle identification based on multi-channel lbp on muzzle images. In 2018 International Conference on Sustainable Information Engineering and Technology (SIET), IEEE Publishing, pp:1-5. DOI: https://doi.org/10.1109/SIET.2018.8693161
  • Li G, Erickson G E & Xiong Y (2022). Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques. Animals, 12(11), DOI: https://doi.org/10.3390/ani12111453
  • Li Z, Lei X & Liu S (2022). A lightweight deep learning model for cattle face recognition. Computers and Electronics in Agriculture, 195 (2022), DOI: https://doi.org/10.1016/j.compag.2022.106848
  • Lu J, Behbood V, Hao P, Zuo H, Xue S & Zhang G (2015). Transfer learning using computational intelligence: A survey. Knowledge-Based Systems, 80 (2015), pp: 14-23, https://doi.org/10.1016/j.knosys.2015.01.010
  • Mikołajczyk A & Grochowski M (2018). Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW), IEEE publishing, pp. 117-122, Poland. DOI: https://doi.org/10.1109/IIPHDW.2018.8388338
  • Noonan G J, Rand J S, Priest J, Ainscattle J & Blackshaw J K (1994). Behavioural observations of piglets undergoing tail docking, teeth clipping and ear notching. Applied Animal Behaviour Science, 39(3-4), 203-213. DOI: https://doi.org/10.1016/0168-1591(94)90156-2
  • Poggio T, Kawaguchi K, Liao Q, Miranda B, Rosasco L, Boix X & Mhaskar H (2018). Theory of deep learning III: ex-plaining the non-overfitting puzzle. arXiv preprint arXiv:1801.00173. DOI: https://doi.org/10.48550/arXiv.1801.00173
  • Polat H E (2022) New Technologies in Good Agricultural Practices – Smart Farming (In Turkish). In: Yaldız G, Çamlıca M (Eds.), Innovative Approaches in Medicinal and Aromatic Plants Production. Iksad Publications, Ankara/Turkey pp: 27- 54. ISBN:978-625-8246-33-9
  • Psota E T, Luc E K, Pighetti G M, Schneider L G, Fryxell R T, Keele J W & Kuehn L A (2021). Development and validation of a neural network for the automated detection of horn flies on cattle. Computers and Electronics in Agriculture, 180 (2021), DOI: https://doi.org/10.1016/j.compag.2020.105927
  • Punn N S & Agarwal S (2021). Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks. Applied Intelligence, 51(5), 2689-2702
  • Huang G, Liu Z, van der Maaten L &Weinberger K Q (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. arXiv. pp: 4700-4708. DOI: https://doi.org/10.48550/arXiv.1608.06993
  • Jiang B, Wu Q, Yin X, Wu D, Song H & He D (2019). FLYOLOv3 deep learning for key parts of dairy cow body detection. Computers and Electronics in Agriculture 166 (2019), DOI: https://doi.org/10.1016/j.compag.2019.104982
  • Qiao Y, Clark C, Lomax S, Kong H, Su D & Sukkarieh S (2021). Automated Individual Cattle Identification Using Video Data: A Unified Deep Learning Architecture Approach. Front. Anim. Sci., 2 (2021), https://doi.org/10.3389/fanim.2021.759147.
  • Rice L, Wong E & Kolter Z (2020). Overfitting in adversarially robust deep learning. Proceedings of the 37 th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020. pp. 8093-8104. http://proceedings.mlr.press/v119/rice20a
  • Ruiz-Garcia L & Lunadei L (2011). The role of RFID in agriculture: Applications, limitations and challenges. Computers and Electronics in Agriculture, 79(1), 42-50. DOI: https://doi.org/10.1016/j.compag.2011.08.010
  • Sandler M, Howard A, Zhu M, Zhmoginov A & Chen L C (2018). MobilenetV2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4510–4520. DOI: https://doi.org/10.48550/arXiv.1801.04381
  • Schmidt L, Santurkar S, Tsipras D, Talwar K & Madry A (2018). Adversarially robust generalization requires more data. In Advances in Neural Information Processing Systems, pp. 5014–5026. https://proceedings.neurips.cc/paper/2018/hash/f708f064faaf32a43e4d3c784e6af9ea-Abstract.html
  • Selvaraju R R, Das A, Vedantam R, Cogswell M, Parikh D & Batra D (2016). Grad-CAM: Why did you say that? arXiv preprint arXiv:1611.07450
  • Shahi T B, Sitaula C, Neupane A & Guo W (2022). Fruit classification using attention-based MobileNetV2 for industrial applications. Plos one, 17(2), DOI: https://doi.org/10.1371/journal.pone.0264586
  • Shen W, Hu H, Dai B Wei X, Sun J, Jiang L & Sun Y (2020). Individual identification of dairy cows based on convolutional neural networks. Multimed Tools Appl (79) 14711–14724, https://doi.org/10.1007/s11042-019-7344-7
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Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Hassas Tarım Teknolojileri, Hayvan Refahı
Bölüm Makaleler
Yazarlar

Havva Eylem Polat 0000-0002-2159-0666

Dilara Gerdan Koc 0000-0002-2705-299X

Ömer Ertuğrul 0000-0003-0774-1728

Caner Koç 0000-0002-9096-4254

Kamil Ekinci

Yayımlanma Tarihi 14 Ocak 2025
Gönderilme Tarihi 3 Temmuz 2024
Kabul Tarihi 23 Ağustos 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 31 Sayı: 1

Kaynak Göster

APA Polat, H. E., Gerdan Koc, D., Ertuğrul, Ö., Koç, C., vd. (2025). Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning. Journal of Agricultural Sciences, 31(1), 137-150. https://doi.org/10.15832/ankutbd.1509798
AMA Polat HE, Gerdan Koc D, Ertuğrul Ö, Koç C, Ekinci K. Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning. J Agr Sci-Tarim Bili. Ocak 2025;31(1):137-150. doi:10.15832/ankutbd.1509798
Chicago Polat, Havva Eylem, Dilara Gerdan Koc, Ömer Ertuğrul, Caner Koç, ve Kamil Ekinci. “Deep Learning Based Individual Cattle Face Recognition Using Data Augmentation and Transfer Learning”. Journal of Agricultural Sciences 31, sy. 1 (Ocak 2025): 137-50. https://doi.org/10.15832/ankutbd.1509798.
EndNote Polat HE, Gerdan Koc D, Ertuğrul Ö, Koç C, Ekinci K (01 Ocak 2025) Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning. Journal of Agricultural Sciences 31 1 137–150.
IEEE H. E. Polat, D. Gerdan Koc, Ö. Ertuğrul, C. Koç, ve K. Ekinci, “Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning”, J Agr Sci-Tarim Bili, c. 31, sy. 1, ss. 137–150, 2025, doi: 10.15832/ankutbd.1509798.
ISNAD Polat, Havva Eylem vd. “Deep Learning Based Individual Cattle Face Recognition Using Data Augmentation and Transfer Learning”. Journal of Agricultural Sciences 31/1 (Ocak 2025), 137-150. https://doi.org/10.15832/ankutbd.1509798.
JAMA Polat HE, Gerdan Koc D, Ertuğrul Ö, Koç C, Ekinci K. Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning. J Agr Sci-Tarim Bili. 2025;31:137–150.
MLA Polat, Havva Eylem vd. “Deep Learning Based Individual Cattle Face Recognition Using Data Augmentation and Transfer Learning”. Journal of Agricultural Sciences, c. 31, sy. 1, 2025, ss. 137-50, doi:10.15832/ankutbd.1509798.
Vancouver Polat HE, Gerdan Koc D, Ertuğrul Ö, Koç C, Ekinci K. Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning. J Agr Sci-Tarim Bili. 2025;31(1):137-50.

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