Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications

This article belongs to the Special Issue Remote Sensing in Viticulture.

Detalhes bibliográficos
Autores: Mesas-Carrascosa, Francisco Javier, Castro, Ana Isabel de, Torres-Sánchez, Jorge, Triviño-Tarradas, Paula, Jiménez-Brenes, Francisco Manuel, García-Ferrer, Alfonso, López Granados, Francisca
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/227042
Acesso em linha:http://hdl.handle.net/10261/227042
Access Level:acceso abierto
Palavra-chave:UAV imagery
Grapevine height
DSM
RGB sensor
Structure
Vineyard
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spelling Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing ApplicationsMesas-Carrascosa, Francisco JavierCastro, Ana Isabel deTorres-Sánchez, JorgeTriviño-Tarradas, PaulaJiménez-Brenes, Francisco ManuelGarcía-Ferrer, AlfonsoLópez Granados, FranciscaUAV imageryGrapevine heightDSMRGB sensorStructureVineyardThis article belongs to the Special Issue Remote Sensing in Viticulture.Remote sensing applied in the digital transformation of agriculture and, more particularly, in precision viticulture offers methods to map field spatial variability to support site-specific management strategies; these can be based on crop canopy characteristics such as the row height or vegetation cover fraction, requiring accurate three-dimensional (3D) information. To derive canopy information, a set of dense 3D point clouds was generated using photogrammetric techniques on images acquired by an RGB sensor onboard an unmanned aerial vehicle (UAV) in two testing vineyards on two different dates. In addition to the geometry, each point also stores information from the RGB color model, which was used to discriminate between vegetation and bare soil. To the best of our knowledge, the new methodology herein presented consisting of linking point clouds with their spectral information had not previously been applied to automatically estimate vine height. Therefore, the novelty of this work is based on the application of color vegetation indices in point clouds for the automatic detection and classification of points representing vegetation and the later ability to determine the height of vines using as a reference the heights of the points classified as soil. Results from on-ground measurements of the heights of individual grapevines were compared with the estimated heights from the UAV point cloud, showing high determination coefficients (R² > 0.87) and low root-mean-square error (0.070 m). This methodology offers new capabilities for the use of RGB sensors onboard UAV platforms as a tool for precision viticulture and digitizing applications.This research was funded by the AGL2017-82335-C4-4R project (Spanish Ministry of Science, Innovation and Universities, AEI-EU FEDER funds).Multidisciplinary Digital Publishing InstituteMinisterio de Ciencia, Innovación y Universidades (España)European CommissionAgencia Estatal de Investigación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2021202120202021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/227042reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/AGL2017-82335-C4-4-Rhttp://doi.org/10.3390/rs12020317Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2270422026-05-22T06:33:51Z
dc.title.none.fl_str_mv Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications
title Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications
spellingShingle Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications
Mesas-Carrascosa, Francisco Javier
UAV imagery
Grapevine height
DSM
RGB sensor
Structure
Vineyard
title_short Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications
title_full Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications
title_fullStr Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications
title_full_unstemmed Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications
title_sort Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications
dc.creator.none.fl_str_mv Mesas-Carrascosa, Francisco Javier
Castro, Ana Isabel de
Torres-Sánchez, Jorge
Triviño-Tarradas, Paula
Jiménez-Brenes, Francisco Manuel
García-Ferrer, Alfonso
López Granados, Francisca
author Mesas-Carrascosa, Francisco Javier
author_facet Mesas-Carrascosa, Francisco Javier
Castro, Ana Isabel de
Torres-Sánchez, Jorge
Triviño-Tarradas, Paula
Jiménez-Brenes, Francisco Manuel
García-Ferrer, Alfonso
López Granados, Francisca
author_role author
author2 Castro, Ana Isabel de
Torres-Sánchez, Jorge
Triviño-Tarradas, Paula
Jiménez-Brenes, Francisco Manuel
García-Ferrer, Alfonso
López Granados, Francisca
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
European Commission
Agencia Estatal de Investigación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv UAV imagery
Grapevine height
DSM
RGB sensor
Structure
Vineyard
topic UAV imagery
Grapevine height
DSM
RGB sensor
Structure
Vineyard
description This article belongs to the Special Issue Remote Sensing in Viticulture.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/227042
url http://hdl.handle.net/10261/227042
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/AGL2017-82335-C4-4-R
http://doi.org/10.3390/rs12020317
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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