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dc.contributor.authorGené Mola, Jordi
dc.contributor.authorGregorio López, Eduard
dc.contributor.authorAuat Cheein, Fernando A.
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.date.accessioned2020-01-20T09:10:02Z
dc.date.issued2019-11-29
dc.identifier.issn0168-1699
dc.identifier.urihttp://hdl.handle.net/10459.1/67824
dc.description.abstractYield monitoring and geometric characterization of crops provide information about orchard variability and vigor, enabling the farmer to make faster and better decisions in tasks such as irrigation, fertilization, pruning, among others. When using LiDAR technology for fruit detection, fruit occlusions are likely to occur leading to an underestimation of the yield. This work is focused on reducing the fruit occlusions for LiDAR-based approaches, tackling the problem from two different approaches: applying forced air flow by means of an air-assisted sprayer, and using multi-view sensing. These approaches are evaluated in fruit detection, yield prediction and geometric crop characterization. Experimental tests were carried out in a commercial Fuji apple (Malus domestica Borkh. cv. Fuji) orchard. The system was able to detect and localize more than 80% of the visible fruits, predict the yield with a root mean square error lower than 6% and characterize canopy height, width, cross-section area and leaf area. The forced air flow and multi-view approaches helped to reduce the number of fruit occlusions, locating 6.7% and 6.5% more fruits, respectively. Therefore, the proposed system can potentially monitor the yield and characterize the geometry in apple trees. Additionally, combining trials with and without forced air flow and multi-view sensing presented significant advantages for fruit detection as they helped to reduce the number of fruit occlusions.
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. Nufri (especially Santiago Salamero and Oriol Morreres) and Vicens Maquinària Agrícola S.A. are also thanked for their support during data acquisition, and Ernesto Membrillo and Roberto Maturino for their support in dataset labelling.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relationMINECO/PN2013-2016/AGL2013-48297-C2-2-R
dc.relationMINECO/PN20107-2020/RTI2018-094222-B-I00
dc.relation.isformatofVersió postprint del document publicat a: https://doi.org/10.1016/j.compag.2019.105121
dc.relation.ispartofComputers and Electronics in Agriculture, 2020, vol. 168, article number 105121
dc.relation.isreferencedbyhttp://hdl.handle.net/10459.1/68782
dc.rightscc-by-nc-nd (c) Elsevier, 2019
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es
dc.subjectTerrestrial LIDAR scanners
dc.subjectfruit counting
dc.subjectvisió artificial
dc.subjectPrecision agriculture
dc.subjectYield prediction
dc.titleFruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2020-01-20T09:10:02Z
dc.identifier.idgrec029444
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccess
dc.identifier.doihttps://doi.org/10.1016/j.compag.2019.105121
dc.date.embargoEndDate2021-11-29


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cc-by-nc-nd (c) Elsevier, 2019
Except where otherwise noted, this item's license is described as cc-by-nc-nd (c) Elsevier, 2019