{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T14:04:58Z","timestamp":1779545098756,"version":"3.53.1"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100015282","name":"CARS","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015282","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":[[2026,7]]},"DOI":"10.1016\/j.compag.2026.111832","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T14:23:24Z","timestamp":1778163804000},"page":"111832","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Video-level beef cattle weight estimation based on RGB-D sequences via Cross-Modal Spatial Registration and Frame Quality Refinement"],"prefix":"10.1016","volume":"249","author":[{"given":"Shihao","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunyu","family":"Gou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinjie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weizheng","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9917-2053","authenticated-orcid":false,"given":"Baisheng","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.compag.2026.111832_b1","doi-asserted-by":"crossref","first-page":"72","DOI":"10.3390\/jimaging10030072","article-title":"Analyzing data modalities for cattle weight estimation using deep learning models","volume":"10","author":"Afridi","year":"2024","journal-title":"J. Imaging"},{"key":"10.1016\/j.compag.2026.111832_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109976","article-title":"Cattle weight estimation model through readily photos","volume":"143","author":"Bai","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"4","key":"10.1016\/j.compag.2026.111832_b3","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":"10.1016\/j.compag.2026.111832_b4","article-title":"Cattle weight estimation using 2D side-view images and estimated depth-based 3D modeling","volume":"12","author":"Botazzo Rozendo","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.compag.2026.111832_b5","series-title":"DeMamba: AI-generated video detection on million-scale GenVideo benchmark","author":"Chen","year":"2024"},{"key":"10.1016\/j.compag.2026.111832_b6","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.biosystemseng.2021.02.001","article-title":"Image-based body mass prediction of heifers using deep neural networks","volume":"204","author":"Dohmen","year":"2021","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.compag.2026.111832_b7","series-title":"Advances in Pig Welfare","first-page":"261","article-title":"Transport of pigs to slaughter and associated handling","author":"Faucitano","year":"2018"},{"key":"10.1016\/j.compag.2026.111832_b8","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2022.947176","article-title":"Supervised learning techniques for dairy cattle body weight prediction from 3D digital images","volume":"13","author":"Gebreyesus","year":"2023","journal-title":"Front. Genet."},{"key":"10.1016\/j.compag.2026.111832_b9","series-title":"2020 International Joint Conference on Neural Networks","first-page":"1","article-title":"Deep learning techniques for beef cattle body weight prediction","author":"Gjergji","year":"2020"},{"issue":"4","key":"10.1016\/j.compag.2026.111832_b10","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.3390\/ani5040409","article-title":"How farm animals react and perceive stressful situations such as handling, restraint, and transport","volume":"5","author":"Grandin","year":"2015","journal-title":"Animals"},{"issue":"4","key":"10.1016\/j.compag.2026.111832_b11","doi-asserted-by":"crossref","first-page":"611","DOI":"10.3390\/ani13040611","article-title":"On-barn forecasting beef cattle production based on automated non-contact body measurement system","volume":"13","author":"Gritsenko","year":"2023","journal-title":"Animals"},{"key":"10.1016\/j.compag.2026.111832_b12","series-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu","year":"2023"},{"key":"10.1016\/j.compag.2026.111832_b13","series-title":"Computer Vision \u2013 ECCV 2014","first-page":"345","article-title":"Learning rich features from RGB-D images for object detection and segmentation","volume":"Vol. 8695","author":"Gupta","year":"2014"},{"key":"10.1016\/j.compag.2026.111832_b14","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.compind.2018.02.011","article-title":"Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device","volume":"98","author":"Hansen","year":"2018","journal-title":"Comput. Ind."},{"key":"10.1016\/j.compag.2026.111832_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107986","article-title":"Two-stream cross-attention vision Transformer based on RGB-D images for pig weight estimation","volume":"212","author":"He","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111832_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110033","article-title":"Learning-based estimation of cattle weight gain and its influencing factors","volume":"231","author":"Hossain","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111832_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108184","article-title":"Body weight estimation of beef cattle with 3D deep learning model: PointNet++","volume":"213","author":"Hou","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111832_b18","series-title":"Proceedings of the 9th International Conference on Computational Science and Technology","first-page":"151","article-title":"Smart cattle: Cattle live weight estimation based on a deep learning approach","volume":"Vol. 983","author":"Jaini","year":"2023"},{"key":"10.1016\/j.compag.2026.111832_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110182","article-title":"FUSE: A framework for uncertainty-aware object assessment from image sequences in uncontrolled environments","volume":"234","author":"Lamping","year":"2025","journal-title":"Comput. Electron. Agric."},{"issue":"10","key":"10.1016\/j.compag.2026.111832_b20","doi-asserted-by":"crossref","first-page":"7349","DOI":"10.1007\/s11760-024-03398-5","article-title":"Estimation of cattle weight from composite image\/height\/length data with spatial and channel attention convolution network (SCA-ConvNet)","volume":"18","author":"Lan","year":"2024","journal-title":"Signal Image Video Process."},{"issue":"5","key":"10.1016\/j.compag.2026.111832_b21","doi-asserted-by":"crossref","first-page":"2896","DOI":"10.3390\/app13052896","article-title":"Cattle weight estimation using fully and weakly supervised segmentation from 2D images","volume":"13","author":"Lee","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.compag.2026.111832_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109773","article-title":"Cow depth image restoration method based on RGB guided network with modulation branch in the cowshed environment","volume":"229","author":"Li","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.111832_b23","article-title":"Feature extraction using multi-view video analytics for dairy cattle body weight estimation","volume":"6","author":"Liu","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.compag.2026.111832_b24","series-title":"VMamba: Visual state space model","author":"Liu","year":"2024"},{"key":"10.1016\/j.compag.2026.111832_b25","doi-asserted-by":"crossref","first-page":"30","DOI":"10.3389\/fsufs.2019.00030","article-title":"Using 3D imaging and machine learning to predict liveweight and carcass characteristics of live finishing beef cattle","volume":"3","author":"Miller","year":"2019","journal-title":"Front. Sustain. Food Syst."},{"issue":"10","key":"10.1016\/j.compag.2026.111832_b26","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.3390\/electronics11101663","article-title":"Automatic weight prediction system for Korean cattle using Bayesian ridge algorithm on RGB-D image","volume":"11","author":"Na","year":"2022","journal-title":"Electronics"},{"key":"10.1016\/j.compag.2026.111832_b27","series-title":"Computer Vision \u2013 ECCV 2022","first-page":"1","article-title":"Expanding language-image pretrained models for general video recognition","volume":"Vol. 13664","author":"Ni","year":"2022"},{"key":"10.1016\/j.compag.2026.111832_b28","article-title":"Integrating deep learning and mobile imaging for assessment of automated conformational indices and weight prediction in Brahman cattle","volume":"12","author":"Nilchuen","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.compag.2026.111832_b29","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.biosystemseng.2017.11.014","article-title":"3D computer-vision system for automatically estimating heifer height and body mass","volume":"173","author":"Nir","year":"2018","journal-title":"Biosyst. Eng."},{"key":"10.1016\/j.compag.2026.111832_b30","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."},{"issue":"11","key":"10.1016\/j.compag.2026.111832_b31","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.3390\/agriculture12111794","article-title":"Live weight prediction of cattle based on deep regression of RGB-D images","volume":"12","author":"Ruchay","year":"2022","journal-title":"Agriculture"},{"issue":"8","key":"10.1016\/j.compag.2026.111832_b32","first-page":"277","article-title":"Non-contact predicting method of dairy cow weight based on Cow-DETR and deep image","volume":"54","author":"Shen","year":"2023","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"issue":"3","key":"10.1016\/j.compag.2026.111832_b33","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.applanim.2005.05.007","article-title":"The importance of straw for pig and cattle welfare: A review","volume":"92","author":"Tuyttens","year":"2005","journal-title":"Appl. Anim. Behav. Sci."},{"issue":"2","key":"10.1016\/j.compag.2026.111832_b34","doi-asserted-by":"crossref","DOI":"10.1093\/jas\/skab022","article-title":"ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images","volume":"99","author":"Wang","year":"2021","journal-title":"J. Anim. Sci."},{"key":"10.1016\/j.compag.2026.111832_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105804","article-title":"Cattle weight estimation using active contour models and regression trees bagging","volume":"179","author":"Weber","year":"2020","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"10.1016\/j.compag.2026.111832_b36","doi-asserted-by":"crossref","DOI":"10.1093\/tas\/txad085","article-title":"Estimating body weight and body condition score of mature beef cows using depth images","volume":"7","author":"Xiong","year":"2023","journal-title":"Transl. Anim. Sci."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926004278?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926004278?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T13:06:45Z","timestamp":1779541605000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926004278"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":36,"alternative-id":["S0168169926004278"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.111832","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Video-level beef cattle weight estimation based on RGB-D sequences via Cross-Modal Spatial Registration and Frame Quality Refinement","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.111832","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 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":"111832"}}