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dc.contributor.authorGené Mola, Jordi
dc.contributor.authorGregorio López, Eduard
dc.contributor.authorAuat Cheein, Fernando
dc.contributor.authorGuevara, Javier
dc.contributor.authorLlorens Calveras, Jordi
dc.contributor.authorSanz Cortiella, Ricardo
dc.contributor.authorEscolà i Agustí, Alexandre
dc.contributor.authorRosell Polo, Joan Ramon
dc.coverage.spatialFuji apple orchard, Agramunt, Catalonia, Spainca_ES
dc.coverage.temporal2017-09ca_ES
dc.date.accessioned2020-05-12T08:14:48Z
dc.date.available2020-05-12T08:14:48Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/10459.1/68782
dc.descriptionDades primàries associades a un article publicat a la revista Computers and Electronics in Agriculture disponible a l'adreça https://doi.org/10.1016/j.compag.2019.105121 i a la revista Data in Brief disponible a l'adreça https://doi.org/10.1016/j.dib.2020.105248
dc.description.abstractThe LFuji-air dataset contains LiDAR based point clouds of 11 Fuji apples trees and the corresponding apples location ground truth. A mobile terrestrial laser scanner (MTLS) comprised of a LiDAR sensor and a real-time kinematics global navigation satellite system was used to acquire the data. The MTLS was mounted on an air-assisted sprayer used to generate different air flow conditions. A total of 8 scans per tree were performed, including scans from different LiDAR sensor positions (multi-view approach) and under different air flow conditions. These variability of the scanning conditions allows to use the LFuji-air dataset not only for training and testing new fruit detection algorithms, but also to study the usefullness of the multi-view approach and the application of forced air flow to reduce the number of fruit oclusions.ca_ES
dc.description.sponsorshipThis work was partly funded by the Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Economy and Competitiveness (project AGL2013-48297-C2-2-R) and the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00). The Spanish Ministry of Education is thanked for Mr. J. Gené’s pre-doctoral fellowships (FPU15/03355). The work of Jordi Llorens was supported by the Spanish Ministry of Economy, Industry and Competitiveness through a postdoctoral position named Juan de la Cierva Incorporación (JDCI-2016-29464_N18003). We would also like to thank CONICYT FONDECYT 1171431 and CONICYT FB0008.
dc.language.isoengca_ES
dc.publisherUniversitat de Lleidaca_ES
dc.relationMINECO/PN2013-2016/AGL2013-48297-C2-2-Rca_ES
dc.relationMINECO/PN2013-2016/RTI2018-094222-B-I00ca_ES
dc.relation.isreferencedbyhttp://hdl.handle.net/10459.1/67824
dc.relation.isreferencedbyhttp://hdl.handle.net/10459.1/68042
dc.rightscc-by-nc-sa (c) Jordi Gené et al., 2020ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectFruit detectionca_ES
dc.subjectYield predictionca_ES
dc.subjectLiDARca_ES
dc.subjectMTLSca_ES
dc.subjectFruit reflectanceca_ES
dc.subjectComputer visionca_ES
dc.titleLFuji-air dataset [Research data] ca_ES
dc.typeinfo:eu-repo/semantics/otherca_ES
dc.typeinfo:eu-repo/semantics/datasetca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES


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cc-by-nc-sa (c) Jordi Gené et al., 2020
Except where otherwise noted, this item's license is described as cc-by-nc-sa (c) Jordi Gené et al., 2020