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

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.

Keywords

• : Cattle identification
• deep learning
• face detection
• smart farming

References

1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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
8. 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

9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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.
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. Srivastava N, Hinton G, Krizhevsky A, Sutskever I & Salakhutdinov R (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1):1929–1958, DOI: https://dl.acm.org/doi/abs/10.5555/2627435.2670313
41. Szegedy C, Vanhoucke V, Ioffe S, Shlens J & Wojna Z (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826. https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.html
42. Tsai H, Ambrogio S, Narayanan P, Shelby R M & Burr G W (2018). Recent progress in analog memory-based accelerators for deep learning. Journal of Physics D: Applied Physics, 51(28), DOI: https://doi.org/10.1088/1361-6463/aac8a5
43. Wang H, Qin J, Hou Q & Gong S (2020). Cattle face recognition method based on parameter transfer and deep learning. In Journal of Physics: Conference Series, 1453 (2020), IOP Publishing, DOI: https://doi.org/10.1088/1742-6596/1453/1/012054
44. Wang J, He X, Faming S, Lu G, Cong H & Jiang Q (2021). A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure. IEEE Access, (9) pp: 93209-93223. DOI: https://doi.org/10.1109/ACCESS.2021.3093210
45. Wang Z, Fu Z, Chen W & Hu J (2010). A RFID-based traceability system for cattle breeding in China. International Conference on Computer Application and System Modeling (ICCASM 2010), IEEE, Taiyuan. DOI: https://doi.org/10.1109/ICCASM.2010.5620675
46. Weng Z, Meng F, Liu S, Zhang Y, Zheng Z & Gong C (2022). Cattle face recognition based on a Two-Branch convolutional neural network. Computers and Electronics in Agriculture, 196 (2022), DOI: https://doi.org/10.1016/j.compag.2022.106871
47. Xiao J, Liu G, Wang K & Si Y (2022). Cow identification in free-stall barns based on an improved Mask R-CNN and an SVM. Computers and Electronics in Agriculture, 194 (2022), DOI: https://doi.org/10.1016/j.compag.2022.106738
48. Xu B, Wang W, Guo L, Chen G, Li Y, Cao Z & Wu S (2022). CattleFaceNet: A cattle face identification approach based on Retina Face and ArcFace loss. Computers and Electronics in Agriculture, 193 (2022), DOI: https://doi.org/10.1016/j.compag.2021.106675
49. Xu B, Wang W, Guo L, Chen G, Wang Y, Zhang W & Li Y (2021). Evaluation of deep learning for automatic multi-view face detection in cattle. Agriculture, 11(11), https://doi.org/10.3390/agriculture11111062
50. Xu M, Yoon S, Fuentes A & Park D S (2023). A comprehensive survey of image augmentation techniques for deep learning. Pattern Recognition, 137 (2023) DOI: https://doi.org/10.1016/j.patcog.2023.109347
51. Yao L, Liu H, Hu Z, Kuang Y, Liu C & Gao Y (2019). Cow face detection and recognition based on automatic feature extraction algorithm. ACM TURC ‘19: Proceedings of the ACM Turing Celebration Conference—China, May 2019, Article No.: 95, Pp 1–5, https://doi.org/10.1145/3321408.3322628
52. Ying W, Zhang Y, Huang J & Yang Q (2018). Transfer learning via learning to transfer. In International Conference on Machine Learning, pp. 5085-5094. Available from https://proceedings.mlr.press/v80/wei18a.html.
53. Zhang C, Bengio S, Hardt M, Recht B & Vinyals O (2016). Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530. DOI: https://doi.org/10.48550/arXiv.1611.03530
54. Zhang J, Sun G, Zheng K & Mazhar S (2020). Pupil detection based on oblique projection using a binocular camera. IEEE Access, vol: 8, pp: 105754-105765, DOI: https://doi.org/10.1109/ACCESS.2020.3000063
55. Zoph B, Vasudevan V, Shlens J & Le Q V (2018). Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8697-8710. DOI: https://doi.org/10.48550/arXiv.1707.07012