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Article

Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle

1
School of Science and Technology, University of New England, Armidale, NSW 2350, Australia
2
College of Science and Engineering, Flinders University, Adelaide, SA 5001, Australia
3
School of IT and Engineering, Melbourne Institute of Technology, Melbourne, VIC 3000, Australia
4
School of Environmental and Rural Science, University of New England, Armidale, NSW 2350, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Roberto Marani
Agronomy 2021, 11(11), 2365; https://doi.org/10.3390/agronomy11112365
Received: 27 October 2021 / Revised: 19 November 2021 / Accepted: 19 November 2021 / Published: 22 November 2021
(This article belongs to the Special Issue Data-Driven Agricultural Innovations)
Livestock welfare and management could be greatly enhanced by the replacement of branding or ear tagging with less invasive visual biometric identification methods. Biometric identification of cattle from muzzle patterns has previously indicated promising results. Significant barriers exist in the translation of these initial findings into a practical precision livestock monitoring system, which can be deployed at scale for large herds. The objective of this study was to investigate and address key limitations to the autonomous biometric identification of cattle. The contributions of this work are fourfold: (1) provision of a large publicly-available dataset of cattle face images (300 individual cattle) to facilitate further research in this field, (2) development of a two-stage YOLOv3-ResNet50 algorithm that first detects and extracts the cattle muzzle region in images and then applies deep transfer learning for biometric identification, (3) evaluation of model performance across a range of cattle breeds, and (4) utilizing few-shot learning (five images per individual) to greatly reduce both the data collection requirements and duration of model training. Results indicated excellent model performance. Muzzle detection accuracy was 99.13% (1024 × 1024 image resolution) and biometric identification achieved 99.11% testing accuracy. Overall, the two-stage YOLOv3-ResNet50 algorithm proposed has substantial potential to form the foundation of a highly accurate automated cattle biometric identification system, which is applicable in livestock farming systems. The obtained results indicate that utilizing livestock biometric monitoring in an advanced manner for resource management at multiple scales of production is possible for future agriculture decision support systems, including providing useful information to forecast acceptable stocking rates of pastures. View Full-Text
Keywords: biometric identification; cattle; deep learning; few-shot learning; livestock welfare; muzzle detection; precision livestock farming biometric identification; cattle; deep learning; few-shot learning; livestock welfare; muzzle detection; precision livestock farming
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MDPI and ACS Style

Shojaeipour, A.; Falzon, G.; Kwan, P.; Hadavi, N.; Cowley, F.C.; Paul, D. Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle. Agronomy 2021, 11, 2365. https://doi.org/10.3390/agronomy11112365

AMA Style

Shojaeipour A, Falzon G, Kwan P, Hadavi N, Cowley FC, Paul D. Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle. Agronomy. 2021; 11(11):2365. https://doi.org/10.3390/agronomy11112365

Chicago/Turabian Style

Shojaeipour, Ali, Greg Falzon, Paul Kwan, Nooshin Hadavi, Frances C. Cowley, and David Paul. 2021. "Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle" Agronomy 11, no. 11: 2365. https://doi.org/10.3390/agronomy11112365

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