Design of a Cow Body Recognition System Based on Deep Learning

DOI: https://doi.org/10.62517/jike.202404113

Author(s)

Jun Zhu, Jianfei Shi*, Lupeng Xu, Fan Liu, Xiaoyu Xu, Changjun Zhang

Affiliation(s)

Heilongjiang Bayi Agricultural University, School of Information and Electrical Engineering, Daqing, China *Corresponding Author.

Abstract

With the advancement of modern animal husbandry in China, it is particularly important to establish smart farms through technologies such as the internet and 5G communication to achieve fine-grained management. Achieving smart farming through cattle identification is a crucial step. To improve the accuracy of cattle identification in the animal husbandry industry, this project establishes a dataset of cattle body features. Then, a cattle identification system is designed based on computer vision to extract cattle body features. Specifically, the YOLOv5 model is used to detect and extract the image features of cattle bodies. Finally, the detected cattle body image features are compared and updated with the database on the server to complete the cattle identification.

Keywords

Deep Learning; Cattle Body Identification; Artificial Intelligence; YOLOv5 Model; Feature Matching

References

[1] Shi An, Ma Xiaoming, Yang Jian, Kang Xiaoguo, Sun Wenyang.et al. Analysis on the "Smart" Development of Modern Animal Husbandry. Feed Review, 2022 (01): 77-80. [2] Wang Xiaoqiang. Research on Feature Extraction Algorithm for Cattle Eye Iris Recognition. Jiangsu: Southeast University, 2011. [3] Schölkopf B, Smola A, Müller K R. Kernel principal component analysis//International conference on artificial neural networks. Springer, Berlin, Heidelberg, 1997: 583-588. [4] Gaber T, Tharwat A, Hassanien A E, et al. Biometric cattle identification approach based on Weber’s Local Descriptor and AdaBoost classifier. Computers and Electronics in Agriculture, 2016, 122: 55-66. [5] Chen J, Shan S, He C, et al. WLD: A robust local image descriptor. IEEE transactions on pattern analysis and machine intelligence, 2009, 32 (9): 1705-1720. [6] A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 1997, 55 (1): 119-139. [7] Kumar S, Singh S K, Singh A K. Muzzle point pattern based techniques for individual cattle identification. IET Image Processing, 2017, 11 (10): 805-814. [8] Chen Juanjuan, Liu Caixing, Gao Yuefang, et al. Cow Recognition Algorithm Based on Improved Bag of Features Model. Journal of Computer Applications, 2016, 36 (8): 2346-2351 [9] Lazebnik S, Schmid C, Ponce J. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories//2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06). IEEE, 2006, 2: 2169-2178. [10] Dalal N, Triggs B. Histograms of oriented gradients for human detection//2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). Ieee, 52 2005, 1: 886-893. [11] Cortes C, Vapnik V. Support-vector networks. Machine learning, 1995, 20 (3): 273-297. [12] Sun Xi, Zhao Ping. Research on Cow Face Recognition Method Based on Convolutional Neural Network. Public Standardization, 2020 (5): 59-60. [13] Wang Yueming, Du Yanru, Chen Tiantian, Xu Li. Recognition and Statistical Study of Daily Behaviors of Cattle Based on the YOLOv5s Network Model. Heilongjiang Animal Husbandry, 2022, 14 (1): 48-51, 56, 137-139.