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dc.contributor.authorFont Calafell, Davinia
dc.contributor.authorTresánchez Ribes, Marcel
dc.contributor.authorMartínez Lacasa, Daniel
dc.contributor.authorMoreno Blanc, Javier
dc.contributor.authorClotet Bellmunt, Eduard
dc.contributor.authorPalacín Roca, Jordi
dc.date.accessioned2016-10-13T08:59:18Z
dc.date.available2016-10-13T08:59:18Z
dc.date.issued2015
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10459.1/57906
dc.descriptionThis paper presents a method for vineyard yield estimation based on the analysis of high-resolution images obtained with artificial illumination at night. First, this paper assesses different pixel-based segmentation methods in order to detect reddish grapes: threshold based, Mahalanobis distance, Bayesian classifier, linear color model segmentation and histogram segmentation, in order to obtain the best estimation of the area of the clusters of grapes in this illumination conditions. The color spaces tested were the original RGB and the Hue-Saturation-Value (HSV). The best segmentation method in the case of a non-occluded reddish table-grape variety was the threshold segmentation applied to the H layer, with an estimation error in the area of 13.55%, improved up to 10.01% by morphological filtering. Secondly, after segmentation, two procedures for yield estimation based on a previous calibration procedure have been proposed: (1) the number of pixels corresponding to a cluster of grapes is computed and converted directly into a yield estimate; and (2) the area of a cluster of grapes is converted into a volume by means of a solid of revolution, and this volume is converted into a yield estimate; the yield errors obtained were 16% and −17%, respectively.ca_ES
dc.description.sponsorshipThis work was partially funded by the University of Lleida, Indra, the Government of Catalonia (Comisionat per a Universitats i Recerca, Departament d’Innovació, Universitats i Empresa) and the European Social Fund: Ref. 2012FI_B 00301.ca_ES
dc.language.isoengca_ES
dc.publisherMDPIca_ES
dc.relation.isformatofReproducció del document publicat a https://doi.org/10.3390/s150408284ca_ES
dc.relation.ispartofSensors, 2015, vol.15, p. 8284-8301ca_ES
dc.rightscc-by (c) Font et al., 2016ca_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectPrecision agricultureca_ES
dc.subjectYield estimationca_ES
dc.subjectSegmentation techniquesca_ES
dc.subjectColor featuresca_ES
dc.titleVineyard yield estimation based on the analysis of high resolution images obtained with artificial illumination at nightca_ES
dc.typearticleca_ES
dc.identifier.idgrec023738
dc.type.versionpublishedVersionca_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_ES
dc.identifier.doihttps://doi.org/10.3390/s150408284


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cc-by (c) Font et al., 2016
Except where otherwise noted, this item's license is described as cc-by (c) Font et al., 2016