<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-13T07:39:23Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:10459.1/84029" metadataPrefix="qdc">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:10459.1/84029</identifier><datestamp>2024-12-05T22:27:22Z</datestamp><setSpec>com_2072_3622</setSpec><setSpec>col_2072_479130</setSpec></header><metadata><qdc:qualifieddc xmlns:qdc="http://dspace.org/qualifieddc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://purl.org/dc/elements/1.1/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dc.xsd http://purl.org/dc/terms/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dcterms.xsd http://dspace.org/qualifieddc/ http://www.ukoln.ac.uk/metadata/dcmi/xmlschema/qualifieddc.xsd">
   <dc:title>Fruit detection and 3D location using instance segmentation neural networks and SfM photogrammetry</dc:title>
   <dc:creator>Gregorio López, Eduard</dc:creator>
   <dc:creator>Sanz Cortiella, Ricardo</dc:creator>
   <dc:creator>Rosell Polo, Joan Ramon</dc:creator>
   <dc:creator>Morros Rubió, Josep Ramon</dc:creator>
   <dc:creator>Ruiz Hidalgo, Javier</dc:creator>
   <dc:creator>Vilaplana Besler, Verónica</dc:creator>
   <dc:creator>Gregorio López, Eduard</dc:creator>
   <dc:subject>Structure-from-motion</dc:subject>
   <dc:subject>Fruit detection</dc:subject>
   <dc:subject>Fruit location</dc:subject>
   <dc:subject>Mask R-CNN</dc:subject>
   <dc:subject>Terrestrial remote sensing</dc:subject>
   <dcterms:abstract>The development of remote fruit detection systems able to identify and 3D locate fruits provides opportunities to improve the effi-ciency of agriculture management. Most of the current fruit detec-tion systems are based on 2D image analysis. Although the use of 3D sensors is emerging, precise 3D fruit location is still a pending issue. This work presents a new methodology for fruit detection and 3D location, combining the use of instance segmentation neu-ral networks and Structure-from-Motion (SfM) photogrammetry.</dcterms:abstract>
   <dcterms:abstract>This work was partly funded by the Secretaria d’Universitats i Recerca de la Gener-alitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Economy and Competitiveness (projects AGL2013-48297-C2-2-R and TEC2016-75976-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).</dcterms:abstract>
   <dcterms:dateAccepted>2024-12-05T22:27:22Z</dcterms:dateAccepted>
   <dcterms:available>2024-12-05T22:27:22Z</dcterms:available>
   <dcterms:created>2024-12-05T22:27:22Z</dcterms:created>
   <dcterms:issued>2022-10-31T08:43:23Z</dcterms:issued>
   <dcterms:issued>2022-10-31T08:43:23Z</dcterms:issued>
   <dcterms:issued>2020</dcterms:issued>
   <dc:type>info:eu-repo/semantics/conferenceObject</dc:type>
   <dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
   <dc:identifier>http://hdl.handle.net/10459.1/84029</dc:identifier>
   <dc:relation>info: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:relation>
   <dc:relation>info:eu-repo/grantAgreement/MINECO//TEC2016-75976-R/ES/</dc:relation>
   <dc:relation>info: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>
   <dc:relation>7th Annual Catalan Meeting on Computer Vision. September 22, 2020, Universitat Autònoma de Barcelona, (http://acmcv.cat/)</dc:relation>
   <dc:relation>http://hdl.handle.net/10459.1/67802</dc:relation>
   <dc:rights>(c) J. Gené-Mola et al., 2020</dc:rights>
   <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
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