Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques

Guoming Li; Galen E Erickson; Yijie Xiong

doi:10.3390/ani12111453

Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques

Animals (Basel). 2022 Jun 4;12(11):1453. doi: 10.3390/ani12111453.

Authors

Guoming Li¹, Galen E Erickson², Yijie Xiong^{2

3}

Affiliations

¹ Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA.
² Department of Animal Science, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.
³ Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.

Abstract

Individual feedlot beef cattle identification represents a critical component in cattle traceability in the supply food chain. It also provides insights into tracking disease trajectories, ascertaining ownership, and managing cattle production and distribution. Animal biometric solutions, e.g., identifying cattle muzzle patterns (unique features comparable to human fingerprints), may offer noninvasive and unique methods for cattle identification and tracking, but need validation with advancement in machine learning modeling. The objectives of this research were to (1) collect and publish a high-quality dataset for beef cattle muzzle images, and (2) evaluate and benchmark the performance of recognizing individual beef cattle with a variety of deep learning models. A total of 4923 muzzle images for 268 US feedlot finishing cattle (>12 images per animal on average) were taken with a mirrorless digital camera and processed to form the dataset. A total of 59 deep learning image classification models were comparatively evaluated for identifying individual cattle. The best accuracy for identifying the 268 cattle was 98.7%, and the fastest processing speed was 28.3 ms/image. Weighted cross-entropy loss function and data augmentation can increase the identification accuracy of individual cattle with fewer muzzle images for model development. In conclusion, this study demonstrates the great potential of deep learning applications for individual cattle identification and is favorable for precision livestock management. Scholars are encouraged to utilize the published dataset to develop better models tailored for the beef cattle industry.

Keywords: animal biometrics; cognitive science; computer vision; machine learning; pattern recognition; precision livestock management.

Grants and funding

This work was partially supported by faculty start-up funds provided internally by the Institution of Agriculture and Natural Resources at University of Nebraska-Lincoln and the College of Agriculture and Life Sciences, Iowa State University. This work was also a product of the Nebraska Agricultural Experiment Station (NEAES) Project Number 29448, sponsored by the Agriculture and Natural Resources Hatch Multistate Enhanced Program.