High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability

[EN] Remotesensing techniques can help reduce time and resources spent collecting samples of crops and analyzing quality variables. The main objective of this work was to demonstrate that it is possible to obtain information on the distribution of must quality variables from conventional photographs...

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Authors: García Fernández, Marta, Sanz Ablanedo, Enoc, Rodríguez Pérez, José Ramón
Format: article
Status:Published version
Publication Date:2021
Country:España
Institution:Universidad de León
Repository:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/17598
Online Access:https://hdl.handle.net/10612/17598
Access Level:Open access
Keyword:Cartografía
Hortofruticultura
Ingeniería agrícola
Topografía
Remote sensing
Drone
RGB imagery
Spectral index
Vineyard zoning
Must quality variable
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network_acronym_str ES
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repository_id_str
spelling High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality VariabilityGarcía Fernández, MartaSanz Ablanedo, EnocRodríguez Pérez, José RamónCartografíaHortofruticulturaIngeniería agrícolaTopografíaRemote sensingDroneRGB imagerySpectral indexVineyard zoningMust quality variable[EN] Remotesensing techniques can help reduce time and resources spent collecting samples of crops and analyzing quality variables. The main objective of this work was to demonstrate that it is possible to obtain information on the distribution of must quality variables from conventional photographs. Georeferenced berry samples were collected and analyzed in the laboratory, and RGB images were taken using a low-cost drone from which an orthoimage was made. Transformation equations were calculated to obtain absolute reflectances for the different bands and to calculate 10 vegetation indices plus two new proposed indices. Correlations for the 12 indices with values for 15 must quality variables were calculated in terms of Pearson’s correlation coefficients. Significant correlations were obtained for 100-berries weight (0.77), malic acid (−0.67), alpha amino nitrogen (−0.59), phenolic maturation index (0.69), and the total polyphenol index (0.62), with 100-berries weight and the total polyphenol index obtaining the best results in the proposed RGB-based vegetation index 2 and RGB-based vegetation index 3. Our findings indicate that must variables important for the production of quality wines can be related to the RGB bands in conventional digital images, potentially improving and aiding management and increasing productivity.SIEducation Department of the Junta de Castilla y León-SpainMDPIIngeniería Cartografica, Geodesica y FotogrametriaEscuela de Ingeniería Agraria y Forestal2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://hdl.handle.net/10612/17598reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónInglésinfo:eu-repo/grantAgreement/Education Department of the Junta de Castilla y León//LE112G18http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/175982026-06-24T12:43:27Z
dc.title.none.fl_str_mv High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability
title High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability
spellingShingle High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability
García Fernández, Marta
Cartografía
Hortofruticultura
Ingeniería agrícola
Topografía
Remote sensing
Drone
RGB imagery
Spectral index
Vineyard zoning
Must quality variable
title_short High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability
title_full High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability
title_fullStr High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability
title_full_unstemmed High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability
title_sort High-Resolution Drone-Acquired RGB Imagery to Estimate Spatial Grape Quality Variability
dc.creator.none.fl_str_mv García Fernández, Marta
Sanz Ablanedo, Enoc
Rodríguez Pérez, José Ramón
author García Fernández, Marta
author_facet García Fernández, Marta
Sanz Ablanedo, Enoc
Rodríguez Pérez, José Ramón
author_role author
author2 Sanz Ablanedo, Enoc
Rodríguez Pérez, José Ramón
author2_role author
author
dc.contributor.none.fl_str_mv Ingeniería Cartografica, Geodesica y Fotogrametria
Escuela de Ingeniería Agraria y Forestal
dc.subject.none.fl_str_mv Cartografía
Hortofruticultura
Ingeniería agrícola
Topografía
Remote sensing
Drone
RGB imagery
Spectral index
Vineyard zoning
Must quality variable
topic Cartografía
Hortofruticultura
Ingeniería agrícola
Topografía
Remote sensing
Drone
RGB imagery
Spectral index
Vineyard zoning
Must quality variable
description [EN] Remotesensing techniques can help reduce time and resources spent collecting samples of crops and analyzing quality variables. The main objective of this work was to demonstrate that it is possible to obtain information on the distribution of must quality variables from conventional photographs. Georeferenced berry samples were collected and analyzed in the laboratory, and RGB images were taken using a low-cost drone from which an orthoimage was made. Transformation equations were calculated to obtain absolute reflectances for the different bands and to calculate 10 vegetation indices plus two new proposed indices. Correlations for the 12 indices with values for 15 must quality variables were calculated in terms of Pearson’s correlation coefficients. Significant correlations were obtained for 100-berries weight (0.77), malic acid (−0.67), alpha amino nitrogen (−0.59), phenolic maturation index (0.69), and the total polyphenol index (0.62), with 100-berries weight and the total polyphenol index obtaining the best results in the proposed RGB-based vegetation index 2 and RGB-based vegetation index 3. Our findings indicate that must variables important for the production of quality wines can be related to the RGB bands in conventional digital images, potentially improving and aiding management and increasing productivity.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10612/17598
url https://hdl.handle.net/10612/17598
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/Education Department of the Junta de Castilla y León//LE112G18
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad de León
instname_str Universidad de León
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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