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Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques

SIMPLE SUMMARY: The ability to identify individual animals has gained great interest in beef feedlots to allow for animal tracking and all applications for precision management of individuals. This study assessed the feasibility and performance of a total of 59 deep learning models in identifying in...

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Autores principales: Li, Guoming, Erickson, Galen E., Xiong, Yijie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9179917/
https://www.ncbi.nlm.nih.gov/pubmed/35681917
http://dx.doi.org/10.3390/ani12111453
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author Li, Guoming
Erickson, Galen E.
Xiong, Yijie
author_facet Li, Guoming
Erickson, Galen E.
Xiong, Yijie
author_sort Li, Guoming
collection PubMed
description SIMPLE SUMMARY: The ability to identify individual animals has gained great interest in beef feedlots to allow for animal tracking and all applications for precision management of individuals. This study assessed the feasibility and performance of a total of 59 deep learning models in identifying individual cattle with muzzle images. The best identification accuracy was 98.7%, and the fastest processing speed was 28.3 ms/image. A dataset containing 268 US feedlot cattle and 4923 muzzle images was published along with this article. This study demonstrates the great potential of using deep learning techniques to identify individual cattle using muzzle images and to support precision beef cattle management. 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.
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spelling pubmed-91799172022-06-10 Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques Li, Guoming Erickson, Galen E. Xiong, Yijie Animals (Basel) Article SIMPLE SUMMARY: The ability to identify individual animals has gained great interest in beef feedlots to allow for animal tracking and all applications for precision management of individuals. This study assessed the feasibility and performance of a total of 59 deep learning models in identifying individual cattle with muzzle images. The best identification accuracy was 98.7%, and the fastest processing speed was 28.3 ms/image. A dataset containing 268 US feedlot cattle and 4923 muzzle images was published along with this article. This study demonstrates the great potential of using deep learning techniques to identify individual cattle using muzzle images and to support precision beef cattle management. 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. MDPI 2022-06-04 /pmc/articles/PMC9179917/ /pubmed/35681917 http://dx.doi.org/10.3390/ani12111453 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Guoming
Erickson, Galen E.
Xiong, Yijie
Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques
title Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques
title_full Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques
title_fullStr Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques
title_full_unstemmed Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques
title_short Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques
title_sort individual beef cattle identification using muzzle images and deep learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9179917/
https://www.ncbi.nlm.nih.gov/pubmed/35681917
http://dx.doi.org/10.3390/ani12111453
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