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. 2023 Dec 15;54:122. doi: 10.1186/s13567-023-01255-w

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© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Pipeline overview of the technology. Every pixel of the image is classified as either pertaining to the individual cattle or not. Machine learning extracts facial features so the computer would know what is background and what is cattle. The system ends up knowing exactly which pixels belong to a particular animal. A bounding box is drawn around the pertinent part of the image and cropped out. This both improves model accuracy and reduces storage and processing costs.