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dc.contributor.authorGené Mola, Jordi
dc.contributor.authorLlorens Calveras, Jordi
dc.contributor.authorRosell Polo, Joan Ramon
dc.contributor.authorGregorio López, Eduard
dc.contributor.authorArnó Satorra, Jaume
dc.contributor.authorSolanelles Batlle, Francesc
dc.contributor.authorMartínez Casasnovas, José Antonio
dc.contributor.authorEscolà i Agustí, Alexandre
dc.date.accessioned2020-12-16T08:58:20Z
dc.date.available2020-12-16T08:58:20Z
dc.date.issued2020-12-10
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10459.1/70097
dc.description.abstractThe use of 3D sensors combined with appropriate data processing and analysis has provided tools to optimise agricultural management through the application of precision agriculture. The recent development of low-cost RGB-Depth cameras has presented an opportunity to introduce 3D sensors into the agricultural community. However, due to the sensitivity of these sensors to highly illuminated environments, it is necessary to know under which conditions RGB-D sensors are capable of operating. This work presents a methodology to evaluate the performance of RGB-D sensors under different lighting and distance conditions, considering both geometrical and spectral (colour and NIR) features. The methodology was applied to evaluate the performance of the Microsoft Kinect v2 sensor in an apple orchard. The results show that sensor resolution and precision decreased significantly under middle to high ambient illuminance (>2000 lx). However, this effect was minimised when measurements were conducted closer to the target. In contrast, illuminance levels below 50 lx affected the quality of colour data and may require the use of artificial lighting. The methodology was useful for characterizing sensor performance throughout the full range of ambient conditions in commercial orchards. Although Kinect v2 was originally developed for indoor conditions, it performed well under a range of outdoor conditions.
dc.description.sponsorshipThis research was funded by the Spanish Ministry of Economy and Competitiveness and the Ministry of Science, Innovation and Universities through the program Plan Estatal I+D+i Orientada a los Retos de la Sociedad, grant numbers AGL2013-48297-C2-2-R and RTI2018-094222-B-I00, respectively.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherMDPI
dc.relationinfo:eu-repo/grantAgreement/MINECO//AGL2013-48297-C2-2-R/ES/HERRAMIENTAS DE BASE FOTONICA PARA LA GESTION AGRONOMICA Y EL USO DE PRODUCTOS FITOSANITARIOS SOSTENIBLE EN CULTIVOS ARBOREOS EN EL MARCO DE LA AGRICULTURA DE PRECISION/
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094222-B-I00/ES/TECNOLOGIAS DE AGRICULTURA DE PRECISION PARA OPTIMIZAR EL MANEJO DEL DOSEL FOLIAR Y LA PROTECCION FITOSANITARIA SOSTENIBLE EN PLANTACIONES FRUTALES/
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.3390/s20247072
dc.relation.ispartofSensors, 2020, vol. 20, num. 7072
dc.relation.isreferencedbyhttp://hdl.handle.net/10459.1/70095
dc.rightscc-by (c) Gené Mola, Jordi et al., 2020
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es
dc.subjectRGB-D cameras
dc.subjectDepth cameras
dc.subjectPrecision agriculture
dc.subjectPlant phenotyping
dc.subjectAgricultural robotics
dc.titleAssessing the Performance of RGB-D Sensors for 3D Fruit Crop Canopy Characterization under Different Operating and Lighting Conditions
dc.typeinfo:eu-repo/semantics/article
dc.date.updated2020-12-16T08:58:20Z
dc.identifier.idgrec030678
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.identifier.doihttps://doi.org/10.3390/s20247072


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