Abstract
The monitoring of farm animals is important as it allows farmers keeping track of the performance indicators and any signs of health issues, which is useful to improve the production of milk, meat, eggs and others. In Europe, bovine identification is mostly dependent upon the electronic ID/RFID ear tags, as opposed to branding and tattooing. The RFID based ear-tagging approach has been called into question because of implementation and management costs, physical damage and animal welfare concerns. In this paper, we conduct a case study for individual identification of Holstein cattle, characterized by black, brown and white patterns, in collaboration with the Dairy campus in Leeuwarden. We use a FLIR E6 thermal camera to collect an infrared and RGB image of the side view of each cow just after leaving the milking station. We apply a fully automatic pipeline, which consists of image processing, computer vision and machine learning techniques on a data set containing 1237 images and 136 classes (i.e. individual animals). In particular, we use the thermal images to segment the cattle from the background and remove horizontal and vertical pipes that occlude the cattle in the station, followed by filling the blank areas with an inpainting algorithm. We use the segmented image and apply transfer learning to a pre-trained AlexNet convolutional neural network. We apply five-fold cross-validation and achieve an average accuracy rate of 0.9754 ± 0.0097. The results obtained suggest that the proposed non-invasive approach is highly effective in the automatic recognition of Holstein cattle from the side view. In principle, this approach is applicable to any farm animals that are characterized by distinctive coat patterns.
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Notes
- 1.
The data set is available here: http://infosysdemos.cs.rug.nl.
References
Andrew, W., Greatwood, C., Burghardt, T.: Visual localisation and individual identification of Holstein Friesian cattle via deep learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2850–2859 (2017)
Awad, A.I., Zawbaa, H.M., Mahmoud, H.A., Nabi, E.H.H.A., Fayed, R.H., Hassanien, A.E.: A robust cattle identification scheme using muzzle print images. In: 2013 Federated Conference on Computer Science and Information Systems, pp. 529–534. IEEE (2013)
Choi, D., An, T.H., Ahn, K., Choi, J.: Driving experience transfer method for end-to-end control of self-driving cars. arXiv preprint arXiv:1809.01822 (2018)
Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR09 (2009)
Edwards, D., Johnston, A., Pfeiffer, D.: A comparison of commonly used ear tags on the ear damage of sheep. Anim. Welf. 10(2), 141–151 (2001)
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115 (2017)
Feng, J., Fu, Z., Wang, Z., Xu, M., Zhang, X.: Development and evaluation on a RFID-based traceability system for cattle/beef quality safety in China. Food control 31(2), 314–325 (2013)
Fosgate, G., Adesiyun, A., Hird, D.: Ear-tag retention and identification methods for extensively managed water buffalo (bubalus bubalis) in trinidad. Prev. Vet. Med. 73(4), 287–296 (2006)
Fu, K.S., Mui, J.: A survey on image segmentation. Pattern Recognit. 13(1), 3–16 (1981)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016). http://www.deeplearningbook.org
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York (2001). https://doi.org/10.1007/978-0-387-21606-5
Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Kumar, S., et al.: Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement 116, 1–17 (2018)
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Lowe, D.G., et al.: Object recognition from local scale-invariant features
Lu, Y., He, X., Wen, Y., Wang, P.S.: A new cow identification system based on iris analysis and recognition. Int. J. Biom. 6(1), 18–32 (2014)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Petersen, W.: The identification of the bovine by means of nose-prints. J. Dairy Sci. 5(3), 249–258 (1922)
Phillips, C.: Cattle Behaviour and Welfare. Wiley, Hoboken (2008)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014). http://jmlr.org/papers/v15/srivastava14a.html
Telea, A.: An image inpainting technique based on the fast marching method. J. Graph. Tools 9(1), 23–34 (2004)
Wamba, S.F., Anand, A., Carter, L.: RFID applications, issues, methods and theory: a review of the AIS basket of TOP journals. Procedia Technol. 9, 421–430 (2013)
Wieslander, H., et al.: Deep convolutional neural networks for detecting cellular changes due to malignancy. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 82–89 (2017)
Acknowledgements
We thank the Dairy campus in Leeuwarden for permitting the data collection used in this project and for approving its availability for academic use.
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Bhole, A., Falzon, O., Biehl, M., Azzopardi, G. (2019). A Computer Vision Pipeline that Uses Thermal and RGB Images for the Recognition of Holstein Cattle. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_10
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