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dc.contributor.authorLavaquiol Colell, Bernat
dc.contributor.authorSanz Cortiella, Ricardo
dc.contributor.authorLlorens Calveras, Jordi
dc.contributor.authorArnó Satorra, Jaume
dc.contributor.authorEscolà i Agustí, Alexandre
dc.date.accessioned2021-11-25T08:50:14Z
dc.date.available2021-11-25T08:50:14Z
dc.date.issued2021-11-05
dc.identifier.issn0168-1699
dc.identifier.urihttp://hdl.handle.net/10459.1/72407
dc.description.abstractIn recent decades, a considerable number of sensors have been developed to obtain 3D point clouds that have great potential in optimizing management in agriculture through the application of precision agriculture techniques. In order to use the data provided by these sensors, it is essential to know their measurement error. In this paper, a methodology is presented for obtaining a 3D point cloud of a central axis training system defoliated fruit tree (Malus domestica Bork.) obtained from stereophotogrammetry techniques based on structure-from-motion (SfM) and multi-view stereo-photogrammetry (MVS). The point cloud was made from a set of 288 photographs of the scene including the ground truth tree which was used to generate the digital 3D model. The resulting point cloud was validated and proven to faithfully represent reality. The bias of the resulting model is −0.15 mm and 0.05 mm, for diameters and lengths, respectively. In addition, the presented methodology allows small changes in the ground truth actual tree to be detected as a consequence of the wood dehydration process. Having an actual and a digital ground-truth is the basis for validating other sensing systems for 3D vegetation characterization which can be used to obtain data to make more informed management decisions.
dc.description.sponsorshipThis research was funded by the Spanish Ministry of Economy and Competitiveness and the Ministry of Science, Innovation and Universities through the program Plan Estatal I+D+i Orientada a los Retos de la Sociedad, Project PAgFRUIT RTI2018-094222-B-I00. In addition, this work was also supported by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya under Grant 2017-SGR-646 and under the research grant program BFC2020S - Programa Santander Predocs UdL 2020. We would also like to thank Jaume Badia from Nufri for providing the tree used in this article as ground truth.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relationMINECO/2017-2020/RTI2018-094222-B-I00
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1016/j.compag.2021.106553
dc.relation.ispartofComputers and Electronics in Agriculture, 2021, vol. 191, p. 106553
dc.rightscc-by-nc-nd (c) Lavaquiol et al., 2021
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es
dc.subjectPhotogrammetry
dc.subjectGround-truth
dc.subjectPrecision agriculture
dc.subject3D sensors
dc.subjectImage-based point cloud
dc.titleA photogrammetry-based methodology to obtain accurate digital ground-truth of leafless fruit trees
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2021-11-25T08:50:14Z
dc.identifier.idgrec031813
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.1016/j.compag.2021.106553


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cc-by-nc-nd (c) Lavaquiol et al., 2021
Except where otherwise noted, this item's license is described as cc-by-nc-nd (c) Lavaquiol et al., 2021