{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:06:43Z","timestamp":1779397603480,"version":"3.53.1"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003627","name":"Rural Development Administration","doi-asserted-by":"publisher","award":["RS-2021-IP421044"],"award-info":[{"award-number":["RS-2021-IP421044"]}],"id":[{"id":"10.13039\/501100003627","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["RS-2019-NR040079"],"award-info":[{"award-number":["RS-2019-NR040079"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["2019R1A6A1A09031717"],"award-info":[{"award-number":["2019R1A6A1A09031717"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003624","name":"Ministry of Agriculture, Food and Rural Affairs","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003624","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003668","name":"Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003668","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Korea Ministry of Science and ICT","doi-asserted-by":"publisher","award":["2020R1A2C2013060"],"award-info":[{"award-number":["2020R1A2C2013060"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Korea Ministry of Science and ICT","doi-asserted-by":"publisher","award":["RS-2024-00392406"],"award-info":[{"award-number":["RS-2024-00392406"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electronics in Agriculture"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1016\/j.compag.2025.110301","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T22:46:58Z","timestamp":1742338018000},"page":"110301","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":10,"special_numbering":"C","title":["Utilizing farm knowledge for indoor precision livestock farming: Time-domain adaptation of cattle face recognition"],"prefix":"10.1016","volume":"234","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8830-3553","authenticated-orcid":false,"given":"Shujie","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alvaro","family":"Fuentes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongbin","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sook","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jucheng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongchae","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong Sun","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2025.110301_b0005","doi-asserted-by":"crossref","unstructured":"An, X., Deng, J., Guo, J., Feng, Z., Zhu, X., Yang, J., Liu, T., 2022. Killing Two Birds with One Stone: Efficient and Robust Training of Face Recognition CNNs by Partial FC, 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4032-4041.","DOI":"10.1109\/CVPR52688.2022.00401"},{"key":"10.1016\/j.compag.2025.110301_b0010","first-page":"3464","article-title":"Simple online and realtime tracking, 2016 IEEE international conference on image processing (ICIP)","author":"Bewley","year":"2016","journal-title":"IEEE"},{"key":"10.1016\/j.compag.2025.110301_b0015","series-title":"Cross-Domain Adaptation for Animal Pose Estimation, 2019 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"9497","author":"Cao","year":"2019"},{"key":"10.1016\/j.compag.2025.110301_b0020","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.3390\/ani12081047","article-title":"Holstein cattle face re-identification unifying global and part feature deep network with attention mechanism","volume":"12","author":"Chen","year":"2022","journal-title":"Animals"},{"issue":"1","key":"10.1016\/j.compag.2025.110301_b0025","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1080\/09712119.2018.1502669","article-title":"Body morphometric measurements in Murrah crossbred buffaloes (Bubalus bubalis)","volume":"46","author":"de Melo","year":"2018","journal-title":"J. Appl. Anim. Res."},{"key":"10.1016\/j.compag.2025.110301_b0030","article-title":"Boosting object detection with zero-shot day-night domain adaptation","author":"Du","year":"2023","journal-title":"arXiv preprint arXiv:2312.01220"},{"key":"10.1016\/j.compag.2025.110301_b0035","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.3390\/ani13122020","article-title":"Multiview monitoring of individual cattle behavior based on action recognition in closed barns using deep learning","volume":"13","author":"Fuentes","year":"2023","journal-title":"Animals"},{"key":"10.1016\/j.compag.2025.110301_b0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108044","article-title":"Deep learning-based multi-cattle tracking in crowded livestock farming using video","volume":"212","author":"Han","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.110301_b0045","unstructured":"Han, S., Fuentes, A., Yoon, S., Jeong, Y., Park, D.S., 2022. Cattle Face Pose Estimation using Landmark Points for Smart Livestock Farming, International Conference on Smartmedia (SMA), Saipan, USA."},{"key":"10.1016\/j.compag.2025.110301_b0050","article-title":"A novel Jinnan individual cattle recognition approach based on mutual attention learning scheme","volume":"120551","author":"Hao","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.compag.2025.110301_b0055","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R., 2017. Mask r-cnn, Proceedings of the IEEE international conference on computer vision, pp. 2961-2969.","DOI":"10.1109\/ICCV.2017.322"},{"key":"10.1016\/j.compag.2025.110301_b0060","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.compag.2025.110301_b0065","doi-asserted-by":"crossref","unstructured":"Hewitt, C., Mahmoud, M., 2019. Pose-Informed Face Alignment for Extreme Head Pose Variations in Animals, 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 1-6.","DOI":"10.1109\/ACII.2019.8925472"},{"key":"10.1016\/j.compag.2025.110301_b0070","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.biosystemseng.2020.02.001","article-title":"Cow identification based on fusion of deep parts features","volume":"192","author":"Hu","year":"2020","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.compag.2025.110301_b0075","series-title":"International Conference on Applied Computing to Support Industry: Innovation and Technology","first-page":"223","article-title":"Multi View Face Detection in Cattle Using Infrared Thermography","author":"Jaddoa","year":"2019"},{"key":"10.1016\/j.compag.2025.110301_b0080","doi-asserted-by":"crossref","unstructured":"Khan, M.H., McDonagh, J., Khan, S., Shahabuddin, M., Arora, A., Khan, F.S., Shao, L., Tzimiropoulos, G., 2020. Animalweb: A large-scale hierarchical dataset of annotated animal faces, Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6939-6948.","DOI":"10.1109\/CVPR42600.2020.00697"},{"key":"10.1016\/j.compag.2025.110301_b0085","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1515\/comp-2019-0010","article-title":"An image processing pipeline to segment iris for unconstrained cow identification system","volume":"9","author":"Larregui","year":"2019","journal-title":"Open Comput. Sci."},{"key":"10.1016\/j.compag.2025.110301_b0090","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.3390\/ani12111453","article-title":"Individual beef cattle identification using muzzle images and deep learning techniques","volume":"12","author":"Li","year":"2022","journal-title":"Animals"},{"issue":"3","key":"10.1016\/j.compag.2025.110301_b0095","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1017\/S1751731119002155","article-title":"Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming","volume":"14","author":"Li","year":"2020","journal-title":"animal"},{"key":"10.1016\/j.compag.2025.110301_b0100","series-title":"Computer Vision\u2013ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23\u201327, 2022, Proceedings","first-page":"89","article-title":"SimCC: A Simple Coordinate Classification Perspective for Human Pose Estimation","author":"Li","year":"2022"},{"key":"10.1016\/j.compag.2025.110301_b0105","article-title":"Dab-detr: dynamic anchor boxes are better queries for detr","author":"Liu","year":"2022","journal-title":"arXiv preprint arXiv:2201.12329"},{"key":"10.1016\/j.compag.2025.110301_b0110","first-page":"1","article-title":"Few-shot Classification with Shared and Private Branches for Cow Face Recognition","author":"Meng","year":"2024"},{"key":"10.1016\/j.compag.2025.110301_b0115","doi-asserted-by":"crossref","DOI":"10.3390\/ani13223588","article-title":"Improving known\u2013unknown cattle\u2019s face recognition for smart livestock farm management","volume":"13","author":"Meng","year":"2023","journal-title":"Animals"},{"key":"10.1016\/j.compag.2025.110301_b0120","doi-asserted-by":"crossref","first-page":"342","DOI":"10.3390\/ai2030021","article-title":"Happy cow or thinking pig? Wur wolf-facial coding platform for measuring emotions in farm animals","volume":"2","author":"Neethirajan","year":"2021","journal-title":"AI"},{"key":"10.1016\/j.compag.2025.110301_b0125","article-title":"A novel low-cost visual ear tag based identification system for precision beef cattle livestock farming","author":"Pretto","year":"2022","journal-title":"Information Process. Agric."},{"key":"10.1016\/j.compag.2025.110301_b0130","doi-asserted-by":"crossref","unstructured":"Qiao, L., Geng, Y., Zhang, Y., Zhang, S., Xu, C., 2022. MobiCFNet: A Lightweight Model for Cattle Face Recognition in Nature, Intelligence Science IV: 5th IFIP TC 12 International Conference, ICIS 2022, Xi'an, China, October 28\u201331, 2022, Proceedings. Springer, pp. 386-394.","DOI":"10.1007\/978-3-031-14903-0_41"},{"key":"10.1016\/j.compag.2025.110301_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107579","article-title":"Cattle body detection based on YOLOv5-ASFF for precision livestock farming","volume":"204","author":"Qiao","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.110301_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2021.106143","article-title":"Intelligent perception for cattle monitoring: a review for cattle identification, body condition score evaluation, and weight estimation","volume":"185","author":"Qiao","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.110301_b0145","unstructured":"Ren, S., He, K., Girshick, R., Sun, J., 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28."},{"key":"10.1016\/j.compag.2025.110301_b0150","first-page":"569","article-title":"Cattle Face Recognition Using Deep Transfer Learning Techniques","author":"Ruchay","year":"2023"},{"key":"10.1016\/j.compag.2025.110301_b0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109306","article-title":"On-barn cattle facial recognition using deep transfer learning and data augmentation","volume":"225","author":"Ruchay","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2025.110301_b0160","doi-asserted-by":"crossref","unstructured":"Sun, X., Xiao, B., Wei, F., Liang, S., Wei, Y., 2018. Integral human pose regression, Proceedings of the European conference on computer vision (ECCV), pp. 529-545.","DOI":"10.1007\/978-3-030-01231-1_33"},{"key":"10.1016\/j.compag.2025.110301_b0165","series-title":"2017 14th Conference on Computer and Robot Vision (CRV)","first-page":"277","article-title":"Bootstrapping labelled dataset construction for cow tracking and behavior analysis","author":"Ter-Sarkisov","year":"2017"},{"key":"10.1016\/j.compag.2025.110301_b0170","series-title":"2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"7464","article-title":"YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors","author":"Wang","year":"2023"},{"key":"10.1016\/j.compag.2025.110301_b0175","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.neucom.2020.10.081","article-title":"Deep face recognition: a survey","volume":"429","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compag.2025.110301_b0180","first-page":"1116","article-title":"Mtfcn: multi-task fully convolutional network for cow face detection","author":"Wang","year":"2020"},{"key":"10.1016\/j.compag.2025.110301_b0185","doi-asserted-by":"crossref","unstructured":"Wei, S.-E., Ramakrishna, V., Kanade, T., Sheikh, Y., 2016. Convolutional pose machines, Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 4724-4732.","DOI":"10.1109\/CVPR.2016.511"},{"key":"10.1016\/j.compag.2025.110301_b0190","series-title":"IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"2498","article-title":"Reflectance-Guided, Contrast-Accumulated Histogram Equalization","author":"Wu","year":"2020"},{"key":"10.1016\/j.compag.2025.110301_b0195","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.3390\/agriculture11111062","article-title":"Evaluation of deep learning for automatic multi-view face detection in cattle","volume":"11","author":"Xu","year":"2021","journal-title":"Agriculture"},{"key":"10.1016\/j.compag.2025.110301_b0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111951","article-title":"Boosting cattle face recognition under uncontrolled scenes by embedding enhancement and optimization","volume":"164","author":"Xu","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.compag.2025.110301_b0205","doi-asserted-by":"crossref","unstructured":"Yao, L., Hu, Z., Liu, C., Liu, H., Kuang, Y., Gao, Y., 2019. Cow face detection and recognition based on automatic feature extraction algorithm, Proceedings of the ACM turing celebration conference-china, pp. 1-5.","DOI":"10.1145\/3321408.3322628"},{"key":"10.1016\/j.compag.2025.110301_b0210","doi-asserted-by":"crossref","unstructured":"Yu, C., Xiao, B., Gao, C., Yuan, L., Zhang, L., Sang, N., Wang, J., 2021. Lite-hrnet: A lightweight high-resolution network, Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 10440-10450.","DOI":"10.1109\/CVPR46437.2021.01030"},{"key":"10.1016\/j.compag.2025.110301_b0215","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., Shum, H.-Y., 2022. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605."},{"key":"10.1016\/j.compag.2025.110301_b0220","unstructured":"Zhang, X., Wan, F., Liu, C., Ji, R., Ye, Q., 2019. Freeanchor: Learning to match anchors for visual object detection. Advances in neural information processing systems 32."},{"key":"10.1016\/j.compag.2025.110301_b0225","article-title":"Siamese GC capsule networks for small sample cow face recognition","author":"Zhang","year":"2023","journal-title":"IEEE Access"}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169925004077?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169925004077?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T12:03:45Z","timestamp":1778673825000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169925004077"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":45,"alternative-id":["S0168169925004077"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2025.110301","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Utilizing farm knowledge for indoor precision livestock farming: Time-domain adaptation of cattle face recognition","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2025.110301","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110301"}}