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dc.contributor.authorColaço, André F.
dc.contributor.authorTrevisan, Rodrigo G.
dc.contributor.authorMolin, José P.
dc.contributor.authorRosell Polo, Joan Ramon
dc.contributor.authorEscolà i Agustí, Alexandre
dc.date.accessioned2017-09-20T07:13:40Z
dc.date.available2017-09-20T07:13:40Z
dc.date.issued2017-07-16
dc.identifier.issn2040-4700
dc.identifier.urihttp://hdl.handle.net/10459.1/60239
dc.descriptionProceedings of the 11th European Conference on Precision Agriculture
dc.description.abstractLiDAR (Light detection and ranging) technology is an alternative to current manual methods of canopy geometry estimations in orange trees. The objective of this work was to compare different types of canopy volume estimations of orange trees, some inspired on manual methods and others based on a LiDAR sensor. A point cloud was generated for 25 individual trees using a laser scanning system. The convex-hull and the alpha-shape surface reconstruction algorithms were tested. LiDAR derived models are able to represent orange trees more accurately than traditional methods. However, results differ significantly from the current manual method. In addition, different 3D modeling algorithms resulted in different canopy volume estimations. Therefore, a new standard method should be developed and established.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherThe Animal Consortium
dc.relation.isformatofVersió preprint del document publicat a: https://doi.org/10.1017/S2040470017001133
dc.relation.ispartofAdvances in Animal Biosciences, 2017, vol. 8, núm. 2, p. 477-480
dc.rights(c) The Animal Consortium, 2017
dc.subjectTree crops
dc.subjectlaser scanner
dc.subjectConvex-hull
dc.subjectalpha-shape
dc.subjectCitrus
dc.titleOrange tree canopy volume estimation by manual and LiDAR-based methods
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2017-09-20T07:13:42Z
dc.identifier.idgrec025836
dc.type.versionsubmittedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.1017/S2040470017001133


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