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dc.contributor.authorMéndez, Valeriano
dc.contributor.authorRosell Polo, Joan Ramon
dc.contributor.authorPascual Roca, Miquel
dc.contributor.authorEscolà i Agustí, Alexandre
dc.description.abstractA process is presented for the vector reconstruction of fruit plantations based on the model developed by Verroust and Lazarus. To solve occlusion problems, the use of a dual graph of local and extended connectivity is proposed. The process allows vegetation variables such as the length and volume of the ligneous structure to be measured, enabling studies such as intensity of pruning operations. The process has been tested against simulated models and real trees with different training systems: open-vase system (peach trees) and central leader hedgerow system (pear trees). The cost of the algorithm will be given by the cost of the implementation of Dijkstra's algorithm, which in its standard version is of potential (O(n2)). Algorithm accuracy was checked against point clouds of virtual trees. The reconstruction was also applied before and after a pruning operation of real trees to enable a study of the evolution of the vegetation indices. Results showed the algorithm to be suitable for multi-tree reconstruction of both central leader and open-vase training systems.ca_ES
dc.description.sponsorshipThis research was partially funded by the Spanish Ministry of Economy and Competitiveness (projects SAFESPRAY: AGL2010-22304-C04-03 and AGVANCE: AGL2013-48297-C2-2-R).ca_ES
dc.relation.isformatofVersió postprint del document publicat a
dc.relation.ispartofBiosystems Engineering, 2016, vol. 148, p. 34–47ca_ES
dc.rightscc-by-nc-nd, (c) Elsevier, 2016ca_ES
dc.subjectMulti-tree reconstructionca_ES
dc.subjectMobile terrestrial laser scannerca_ES
dc.subjectPoint cloudca_ES
dc.subjectTree trainingca_ES
dc.subjectLigneous structureca_ES
dc.titleMulti-tree woody structure reconstruction from mobile terrestrial laser scanner point clouds based on a dual neighbourhood connectivity graph algorithmca_ES

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cc-by-nc-nd, (c) Elsevier, 2016
Except where otherwise noted, this item's license is described as cc-by-nc-nd, (c) Elsevier, 2016