{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T14:38:29Z","timestamp":1793025509809,"version":"4.5.0"},"reference-count":52,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":272,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Precision livestock farming (PLF) increasingly relies on computer vision (CV) to monitor animal behavior, health, and production. However, widespread adoption remains limited by the large quantities of manually labeled data required to train most artificial intelligence (AI) models. This paper reviews AI and CV applications in PLF and presents three data-efficient case studies conducted at a research feedlot at Texas A&amp;M University. The assessment of AI model predictability was conducted with precision, recall, F1-score, and adjusted rand index (ARI). In Case Study 1, a data-efficient Mask R-CNN (region-based convolutional neural network) model pre-trained on the common objects in context (COCO) dataset was applied without modification to detect cattle feeding events at individual feed bunks. Across 98,720 frame-bunk classifications, the system achieved 87.2% precision, 89.1% recall, and an 88.2% F1 score compared to physical bunk sensor ground truth. In Case Study 2, transfer learning on a ResNet backbone was used to classify bunk feed grade across six customized levels (S00\u2013S40) using only 183 labeled images. The model achieved 91% accuracy on an independent verification set of 7,905 images. In Case Study 3, statistical tools, i.e. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and k-means clustering, were applied to identify individual animals at the bunk without manual animal-level annotation. This method and achieved an ARI of 0.96 against expert-verified ground truth, successfully differentiating animals. Integrating all three components, the framework produced a continuous record of individual animal bunk visits and estimated feed intake from a 1-h video. Key remaining challenges include long-term individual animal re-identification under changing appearance and environmental conditions, incorporation of spatiotemporal information to better characterize feeding behavior, and the limited availability of large-scale open datasets for PLF research. Future research will integrate multispectral imaging and audio analytics to improve model robustness and advance individual animal identification for cattle health and nutritional management.<\/jats:p>","DOI":"10.1093\/jas\/skag316","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T11:42:28Z","timestamp":1790682148000},"source":"Crossref","is-referenced-by-count":0,"title":["ASAS-NANP SYMPOSIUM: mathematical modeling in animal nutrition\u2014data-efficient computer vision for precision livestock farming"],"prefix":"10.1093","volume":"104","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3041-6368","authenticated-orcid":false,"given":"Yalong","family":"Pi","sequence":"first","affiliation":[{"name":"Institute of Data Science, Texas A&M University, College Station, TX 77843,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0557-3575","authenticated-orcid":false,"given":"Karun","family":"Kaniyamattam","sequence":"additional","affiliation":[{"name":"Department of Animal Science, Texas A&M University, College Station, TX 77843,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adeolu","family":"Adekunle","sequence":"additional","affiliation":[{"name":"Department of Animal Science, Texas A&M University, College Station, TX 77843,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aniruddh","family":"Adiga","sequence":"additional","affiliation":[{"name":"Institute of Data Science, Texas A&M University, College Station, TX 77843,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1883-4911","authenticated-orcid":false,"given":"Luis O","family":"Tedeschi","sequence":"additional","affiliation":[{"name":"Department of Animal Science, Texas A&M University, College Station, TX 77843,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nick","family":"Duffield","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering & Institute of Data Science, Texas A&M University, College Station, TX 77843,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas A&M University, College Station, TX 77843,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"2026102610271742000_skag316-B1","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: a deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2026102610271742000_skag316-B2","first-page":"54","article-title":"Precision livestock farming: precision feeding technologies and sustainable livestock production","volume":"5","author":"Banhazi","year":"2012","journal-title":"Int J Agric Biol Eng"},{"key":"2026102610271742000_skag316-B3","doi-asserted-by":"publisher","first-page":"101079","DOI":"10.1016\/j.animal.2024.101079","article-title":"Biometric identification of dairy cows via real-time facial recognition","volume":"18","author":"Bergman","year":"2024","journal-title":"Animal"},{"key":"2026102610271742000_skag316-B4","doi-asserted-by":"publisher","first-page":"923","DOI":"10.3389\/fgene.2020.00923","article-title":"Infrared spectrometry as a high-throughput phenotyping technology to predict complex traits in livestock systems","volume":"11","author":"Bresolin","year":"2020","journal-title":"Front Genet"},{"key":"2026102610271742000_skag316-B5","doi-asserted-by":"publisher","first-page":"664","DOI":"10.3168\/jds.2022-22138","article-title":"Assessing optimal frequency for image acquisition in computer vision systems developed to monitor feeding behavior of group-housed Holstein heifers","volume":"106","author":"Bresolin","year":"2023","journal-title":"J Dairy Sci"},{"key":"2026102610271742000_skag316-B6","doi-asserted-by":"publisher","first-page":"101208","DOI":"10.1016\/j.atech.2025.101208","article-title":"Computer vision in precision livestock farming: benchmarking YOLOv9, YOLOv10, YOLOv11, and YOLOv12 for individual cattle identification","volume":"12","author":"Bumb\u00e1lek","year":"2025","journal-title":"Smart Agric Technol"},{"key":"2026102610271742000_skag316-B7","doi-asserted-by":"publisher","first-page":"103904","DOI":"10.1016\/j.livsci.2019.103904","article-title":"Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases","volume":"232","author":"Cominotte","year":"2020","journal-title":"Livest. 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