{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T03:58:19Z","timestamp":1784001499238,"version":"3.55.0"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,6,24]],"date-time":"2027-06-24T00:00:00Z","timestamp":1813795200000},"content-version":"am","delay-in-days":296,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000199","name":"U.S. Department of Agriculture","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007917","name":"USDA Agricultural Research Service","doi-asserted-by":"publisher","award":["58-5082-4-040"],"award-info":[{"award-number":["58-5082-4-040"]}],"id":[{"id":"10.13039\/100007917","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":[[2026,9]]},"DOI":"10.1016\/j.compag.2026.112088","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T13:36:55Z","timestamp":1782308215000},"page":"112088","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["SDrAwberry: Scale-referenced Depth-Anything-3 long-sequence reconstruction for Strawberry canopy volume estimation"],"prefix":"10.1016","volume":"252","author":[{"given":"Zijing","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9420-4789","authenticated-orcid":false,"given":"Won Suk","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruoyao","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry","family":"Medeiros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyoung","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heping","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compag.2026.112088_b0005","doi-asserted-by":"crossref","unstructured":"Brook, A., Tal, Y., Markovich, O., Rybnikova, N., 2021. Canopy Volume as a Tool for Early Detection of Plant Drought and Fertilization Stress: Banana plant fine-phenotype (p. 2021.03.04.433868). bioRxiv. https:\/\/doi.org\/10.1101\/2021.03.04.433868.","DOI":"10.1101\/2021.03.04.433868"},{"key":"10.1016\/j.compag.2026.112088_b0010","doi-asserted-by":"crossref","unstructured":"Cabon, Y., Stoffl, L., Antsfeld, L., Csurka, G., Chidlovskii, B., Revaud, J., Leroy, V., 2025a. MUSt3R: Multi-view Network for Stereo 3D Reconstruction (arXiv:2503.01661). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2503.01661.","DOI":"10.1109\/CVPR52734.2025.00106"},{"key":"10.1016\/j.compag.2026.112088_b0015","doi-asserted-by":"crossref","first-page":"1787","DOI":"10.1109\/ICRA.2019.8794030","article-title":"Robot localization based on aerial images for precision agriculture tasks in crop fields","volume":"2019","author":"Chebrolu","year":"2019","journal-title":"International Conference on Robotics and Automation (ICRA)"},{"issue":"13","key":"10.1016\/j.compag.2026.112088_b0020","article-title":"Strawberry yield prediction based on a deep neural network using high-resolution aerial orthoimages","volume":"11","author":"Chen","year":"2019","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.compag.2026.112088_b0025","unstructured":"CloudCompare (Version v2.11.2). (2024). [Computer software]. http:\/\/www.cloudcompare.org\/."},{"issue":"3","key":"10.1016\/j.compag.2026.112088_b0030","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s13595-019-0873-4","article-title":"Scaling-up individual-level allometric equations to predict stand-level fuel loading in Mediterranean shrublands","volume":"76","author":"De C\u00e1ceres","year":"2019","journal-title":"Ann. For. Sci."},{"issue":"2","key":"10.1016\/j.compag.2026.112088_b0035","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/MRA.2006.1638022","article-title":"Simultaneous localization and mapping: Part I","volume":"13","author":"Durrant-Whyte","year":"2006","journal-title":"IEEE Robotics & Automation Magazine"},{"key":"10.1016\/j.compag.2026.112088_b0040","first-page":"1271","article-title":"Real-time loop closure in 2D LIDAR SLAM","volume":"2016","author":"Hess","year":"2016","journal-title":"IEEE International Conference on Robotics and Automation (ICRA)"},{"key":"10.1016\/j.compag.2026.112088_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2026.111526","article-title":"PheMuT: a phenology-informed, multi-modal time-series model for strawberry yield forecasting","volume":"244","author":"Huang","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112088_b0050","doi-asserted-by":"crossref","unstructured":"Huang, Z., Lee, W.S., Minh, \u00d0., 2025. AI-Driven Plant Tracking and Segmentation for Precise Canopy Estimation in Strawberry Field. 2025 ASABE Annual International Meeting, 1. https:\/\/elibrary.asabe.org\/abstract.asp?aid=55381.","DOI":"10.13031\/aim.202500347"},{"key":"10.1016\/j.compag.2026.112088_b0055","doi-asserted-by":"crossref","unstructured":"Huang, Z., Lee, W.S., Takkellapati, N.C., 2024. Strawberry Canopy Size Estimation with SAM Guided by YOLOv8 Detection. 2024 ASABE Annual International Meeting, 1. https:\/\/elibrary.asabe.org\/abstract.asp?aid=54890.","DOI":"10.13031\/aim.202400181"},{"key":"10.1016\/j.compag.2026.112088_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110501","article-title":"Advanced canopy size estimation in strawberry production: a machine learning approach using YOLOv11 and SAM","volume":"236","author":"Huang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.compag.2026.112088_b0065","doi-asserted-by":"crossref","unstructured":"Huang, Z., Lee, W. S., Zhang, P., 2024. Strawba Yolo: A Specialized Mamba-Based Yolo Model for Strawberry Detection (SSRN Scholarly Paper No. 5081087). Social Science Research Network. https:\/\/doi.org\/10.2139\/ssrn.5081087.","DOI":"10.2139\/ssrn.5081087"},{"key":"10.1016\/j.compag.2026.112088_b0070","article-title":"SASP: Segment any strawberry plant, an end-to-end strawberry canopy volume estimation","volume":"11","author":"Huang","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"10.1016\/j.compag.2026.112088_b0075","doi-asserted-by":"crossref","unstructured":"Ilyas, T., Kim, H., 2021. A Deep Learning Based Approach for Strawberry Yield Prediction via Semantic Graphics. 2021 21st International Conference on Control, Automation and Systems (ICCAS), 1835\u20131841. https:\/\/doi.org\/10.23919\/ICCAS52745.2021.9649871.","DOI":"10.23919\/ICCAS52745.2021.9649871"},{"issue":"5","key":"10.1016\/j.compag.2026.112088_b0080","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1002\/ppp3.10275","article-title":"High-throughput phenotyping for breeding targets\u2014current status and future directions of strawberry trait automation","volume":"4","author":"James","year":"2022","journal-title":"Plants People Planet"},{"key":"10.1016\/j.compag.2026.112088_b0085","doi-asserted-by":"crossref","unstructured":"Keetha, N., M\u00fcller, N., Sch\u00f6nberger, J., Porzi, L., Zhang, Y., Fischer, T., Knapitsch, A., Zauss, D., Weber, E., Antunes, N., Luiten, J., Lopez-Antequera, M., Bul\u00f2, S. R., Richardt, C., Ramanan, D., Scherer, S., & Kontschieder, P., 2025. MapAnything: Universal Feed-Forward Metric 3D Reconstruction (arXiv:2509.13414). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2509.13414.","DOI":"10.1109\/3DV69130.2026.00054"},{"key":"10.1016\/j.compag.2026.112088_b0090","first-page":"14","article-title":"3D gaussian splatting for real-time radiance field rendering","author":"Kerbl","year":"2023","journal-title":"ACM Trans. Graph."},{"issue":"1","key":"10.1016\/j.compag.2026.112088_b0095","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S0378-1127(00)00460-6","article-title":"Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests","volume":"146","author":"Ketterings","year":"2001","journal-title":"For. Ecol. Manage."},{"key":"10.1016\/j.compag.2026.112088_b0100","author":"Kirillov","year":"2023","journal-title":"Segment Anything"},{"key":"10.1016\/j.compag.2026.112088_b0105","unstructured":"Lavan, L., Thiyagarasa, L., Muthugala, U., Silva, R. de., 2025. Crop Spirals: Re-thinking the field layout for future robotic agriculture (arXiv:2509.25091). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2509.25091."},{"key":"10.1016\/j.compag.2026.112088_b0110","doi-asserted-by":"crossref","unstructured":"Leroy, V., Cabon, Y., Revaud, J., 2024. Grounding Image Matching in 3D with MASt3R (arXiv:2406.09756). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2406.09756.","DOI":"10.1007\/978-3-031-73220-1_5"},{"issue":"11","key":"10.1016\/j.compag.2026.112088_b0115","doi-asserted-by":"crossref","first-page":"3401","DOI":"10.3390\/s25113401","article-title":"A review of optical-based three-dimensional reconstruction and multi-source fusion for plant phenotyping","volume":"25","author":"Li","year":"2025","journal-title":"Sensors"},{"key":"10.1016\/j.compag.2026.112088_b0120","unstructured":"Lin, H., Chen, S., Liew, J., Chen, D. Y., Li, Z., Shi, G., Feng, J., Kang, B., 2025. Depth Anything 3: Recovering the Visual Space from Any Views (arXiv:2511.10647). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2511.10647."},{"key":"10.1016\/j.compag.2026.112088_b0125","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.isprsjprs.2019.03.003","article-title":"Vegetation index weighted canopy volume model (CVMVI) for soybean biomass estimation from unmanned aerial system-based RGB imagery","volume":"151","author":"Maimaitijiang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"issue":"1","key":"10.1016\/j.compag.2026.112088_b0130","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1145\/3503250","article-title":"NeRF: representing scenes as neural radiance fields for view synthesis","volume":"65","author":"Mildenhall","year":"2021","journal-title":"Commun. ACM"},{"issue":"11","key":"10.1016\/j.compag.2026.112088_b0135","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.3390\/rs11111261","article-title":"Estimating above-ground biomass of maize using features derived from UAV-based RGB imagery","volume":"11","author":"Niu","year":"2019","journal-title":"Remote Sens. (Basel)"},{"key":"10.1016\/j.compag.2026.112088_b0140","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2019.00147","article-title":"LiDARPheno \u2013 a low-cost LiDAR-based 3D scanning system for leaf morphological trait extraction","volume":"10","author":"Panjvani","year":"2019","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.compag.2026.112088_b0145","article-title":"Deep learning and georeferenced RGB-D imaging for hydroponic strawberry yield mapping","volume":"12","author":"Pardo-Beainy","year":"2025","journal-title":"Smart Agric. Technol."},{"issue":"1","key":"10.1016\/j.compag.2026.112088_b0150","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1186\/s13007-019-0490-0","article-title":"Measuring crops in 3D: using geometry for plant phenotyping","volume":"15","author":"Paulus","year":"2019","journal-title":"Plant Methods"},{"key":"10.1016\/j.compag.2026.112088_b0155","first-page":"4104","article-title":"Structure-from-motion revisited","volume":"2016","author":"Schonberger","year":"2016","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"10.1016\/j.compag.2026.112088_b0160","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., Zheng, E., Frahm, J.-M., Pollefeys, M., 2016. Pixelwise View Selection for Unstructured Multi-View Stereo. In B. Leibe, J. Matas, N. Sebe, & M. Welling (Eds.), Computer Vision \u2013 ECCV 2016 (Vol. 9907, pp. 501\u2013518). Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-319-46487-9_31.","DOI":"10.1007\/978-3-319-46487-9_31"},{"key":"10.1016\/j.compag.2026.112088_b0165","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J., Zheng, E., Pollefeys, M., & Frahm, J.-M., 2016. Pixelwise View Selection for Unstructured Multi-View Stereo (Vol. 9907). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_31.","DOI":"10.1007\/978-3-319-46487-9_31"},{"key":"10.1016\/j.compag.2026.112088_b0170","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, M., Karaev, N., Vedaldi, A., Rupprecht, C., & Novotny, D., 2025. VGGT: Visual Geometry Grounded Transformer (arXiv:2503.11651). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2503.11651.","DOI":"10.1109\/CVPR52734.2025.00499"},{"key":"10.1016\/j.compag.2026.112088_b0175","doi-asserted-by":"crossref","unstructured":"Wang, S., Leroy, V., Cabon, Y., Chidlovskii, B., Revaud, J., 2024. DUSt3R: Geometric 3D Vision Made Easy (arXiv:2312.14132). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2312.14132.","DOI":"10.1109\/CVPR52733.2024.01956"},{"key":"10.1016\/j.compag.2026.112088_b0180","unstructured":"Wang, Y., Zhou, J., Zhu, H., Chang, W., Zhou, Y., Li, Z., Chen, J., Pang, J., Shen, C., He, T., 2025. $\u03c0^3$: Permutation-Equivariant Visual Geometry Learning (arXiv:2507.13347). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2507.13347."},{"key":"10.1016\/j.compag.2026.112088_b0185","doi-asserted-by":"crossref","unstructured":"Yan, H., 2024. A Survey of SLAM based on Submap Strategies. 122\u2013131. https:\/\/doi.org\/10.2991\/978-94-6463-512-6_15.","DOI":"10.2991\/978-94-6463-512-6_15"},{"key":"10.1016\/j.compag.2026.112088_b0190","doi-asserted-by":"crossref","unstructured":"Yang, J., Sax, A., Liang, K. J., Henaff, M., Tang, H., Cao, A., Chai, J., Meier, F., Feiszli, M., 2025. Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass (arXiv:2501.13928). arXiv. https:\/\/doi.org\/10.48550\/arXiv.2501.13928.","DOI":"10.1109\/CVPR52734.2025.02042"},{"key":"10.1016\/j.compag.2026.112088_b0195","article-title":"Estimation of fractional photosynthetically active radiation from a canopy 3D model; case study: almond yield prediction","volume":"12","author":"Zhang","year":"2021","journal-title":"Front. Plant Sci."}],"container-title":["Computers and Electronics in Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926006836?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0168169926006836?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T19:52:44Z","timestamp":1783972364000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0168169926006836"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":39,"alternative-id":["S0168169926006836"],"URL":"https:\/\/doi.org\/10.1016\/j.compag.2026.112088","relation":{},"ISSN":["0168-1699"],"issn-type":[{"value":"0168-1699","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"SDrAwberry: Scale-referenced Depth-Anything-3 long-sequence reconstruction for Strawberry canopy volume estimation","name":"articletitle","label":"Article Title"},{"value":"Computers and Electronics in Agriculture","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compag.2026.112088","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":"112088"}}