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Learn more: PMC Disclaimer | PMC Copyright Notice Front Vet Sci . 2026 Mar 31;13:1810873. doi: 10.3389/fvets.2026.1810873 Search in PMC Search in PubMed View in NLM Catalog Add to search Correction: From machine learning to digital twin integration for livestock production and research Mohamed Abdelrahman Mohamed Abdelrahman 1 Animal Production Department, Faculty of Agriculture, Assuit University, Asyut, Egypt Find articles by Mohamed Abdelrahman 1 , Sali Issa Sali Issa 2 Department of Electrical Information of Science and Technology, Hubei University of Education, Wuhan, China Find articles by Sali Issa 2 , Montaser Elsayed Ali Montaser Elsayed Ali 3 Department of Animal Production, Faculty of Agriculture, Al-Azhar University, Assiut, Egypt Find articles by Montaser Elsayed Ali 3 , Jamal Alotaibi Jamal Alotaibi 4 Department of Computer Engineering, College of Computer, Qassim University, Buraydah, Saudi Arabia Find articles by Jamal Alotaibi 4 , Fahad Alshanbari Fahad Alshanbari 5 Department of Medical Biosciences, College of Veterinary Medicine, Qassim University, Buraydah, Saudi Arabia Find articles by Fahad Alshanbari 5, * Author information Article notes Copyright and License information 1 Animal Production Department, Faculty of Agriculture, Assuit University, Asyut, Egypt 2 Department of Electrical Information of Science and Technology, Hubei University of Education, Wuhan, China 3 Department of Animal Production, Faculty of Agriculture, Al-Azhar University, Assiut, Egypt 4 Department of Computer Engineering, College of Computer, Qassim University, Buraydah, Saudi Arabia 5 Department of Medical Biosciences, College of Veterinary Medicine, Qassim University, Buraydah, Saudi Arabia * Correspondence: Fahad Alshanbari, [email protected] Received 2026 Feb 13; Revised 2026 Feb 18; Accepted 2026 Feb 23; Collection date 2026. Keywords: animal behavior analysis, digital twin, health, livestock production, care, Machine Learning, predictive analytics Copyright © 2026 Abdelrahman, Issa, Elsayed Ali, Alotaibi and Alshanbari. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. PMC Copyright notice PMCID: PMC13078254 PMID: 41988345 This corrects the article " From machine learning to digital twin integration for livestock production and research ", 1744053. There was a mistake in Tables 1 and 2 as published. Table 1. The 195 references distribution by journal/source. Journal/Source No. of articles Animals (MDPI) 34 Sensors (MDPI) 17 Agriculture/Electronics/Applied Sciences (MDPI) 14 Journal of Animal Science 12 Computers and Electronics in Agriculture 11 Journal of Dairy Science 9 Poultry Science 9 Frontiers Journals (Vet Sci, Anim Sci, AI, Genomics) 14 Scientific Reports 8 Animal/Livestock Science/Meat Science 14 Genetics/Genomics Journals 11 AI/Data Science/Engineering Journals 11 Conference Proceedings (IEEE, IFAC, others) 7 Books/Book Chapters (Springer, Elsevier) 4 Other journals (single-occurrence sources) 20 The total 195 Open in a new tab Table 2. Overall summary by livestock category. Category Number of references Poultry 48 Ruminants 79 Pig/multi-species 68 Total 195 Open in a new tab In Table 1 , one category was missing. In Table 2 , the total reference number was updated. The corrected tables appears below. There was a mistake in the caption of Table 1 as published. The corrected caption of Table 1 appears below. “ Table 1 . The 195 references distribution by journal/source.” In Section 2.2, Paragraph 2, there was a mistake in the total reference number. This section has been corrected: “The review mainly focused on original research and review articles, complemented by selected studies for advanced methodological. While searches were in English, the keywords used for relevant articles, including but not limited to: “machine learning,” “deep learning,” “artificial intelligence,” “digital twin,” “precision livestock farming,” “smart farming,” “sensor- based monitoring,” “IoT,” “genomic prediction,” “animal health monitoring,” and “livestock decision support systems.” The initial selection focused on titles and abstracts to eliminate irrelevant records and duplicates. The final dataset comprised 195 references, representing a comprehensive, multi-species, and multidisciplinary set of articles ( Tables 1 , 2 ).” The original version of this article has been updated. Footnotes Edited and reviewed by: Daniel Mota-Rojas , Metropolitan Autonomous University, Mexico Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. 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