A non-invasive method based on computer vision for grapevine cluster compactness assessment using a mobile sensing platform under field conditions

Grapevine cluster compactness affects grape composition, fungal disease incidence, and wine quality. Thus far, cluster compactness assessment has been based on visual inspection performed by trained evaluators with very scarce application in the wine industry. The goal of this work was to develop a...

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Detalles Bibliográficos
Autores: Palacios Arribas, Fernando, Diago, Maria P., Tardáguila, Javier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/202764
Acceso en línea:http://hdl.handle.net/10261/202764
Access Level:acceso abierto
Palabra clave:Image analysis
Cluster morphology
RGB
Machine learning
Non-invasive sensing technologies
Proximal sensing
Precision viticulture
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spelling A non-invasive method based on computer vision for grapevine cluster compactness assessment using a mobile sensing platform under field conditionsPalacios Arribas, FernandoDiago, Maria P.Tardáguila, JavierImage analysisCluster morphologyRGBMachine learningNon-invasive sensing technologiesProximal sensingPrecision viticultureGrapevine cluster compactness affects grape composition, fungal disease incidence, and wine quality. Thus far, cluster compactness assessment has been based on visual inspection performed by trained evaluators with very scarce application in the wine industry. The goal of this work was to develop a new, non-invasive method based on the combination of computer vision and machine learning technology for cluster compactness assessment under field conditions from on-the-go red, green, blue (RGB) image acquisition. A mobile sensing platform was used to automatically capture RGB images of grapevine canopies and fruiting zones at night using artificial illumination. Likewise, a set of 195 clusters of four red grapevine varieties of three commercial vineyards were photographed during several years one week prior to harvest. After image acquisition, cluster compactness was evaluated by a group of 15 experts in the laboratory following the International Organization of Vine and Wine (OIV) 204 standard as a reference method. The developed algorithm comprises several steps, including an initial, semi-supervised image segmentation, followed by automated cluster detection and automated compactness estimation using a Gaussian process regression model. Calibration (95 clusters were used as a training set and 100 clusters as the test set) and leave-one-out cross-validation models (LOOCV; performed on the whole 195 clusters set) were elaborated. For these, determination coefficient (R2) of 0.68 and a root mean squared error (RMSE) of 0.96 were obtained on the test set between the image-based compactness estimated values and the average of the evaluators' ratings (in the range from 1-9). Additionally, the leave-one-out cross-validation yielded a R2 of 0.70 and an RMSE of 1.11. The results show that the newly developed computer vision based method could be commercially applied by the wine industry for efficient cluster compactness estimation from RGB on-the-go image acquisition platforms in commercial vineyards.Fernando Palacios would like to acknowledge the research founding FPI grant 286/2017 by Universidad de La Rioja, Gobierno de La Rioja. Dr Maria P. Diago is funded by the Spanish Ministry of Science, Innovation and Universities with a Ramon y Cajal grant RYC-2015-18429.Multidisciplinary Digital Publishing InstituteUniversidad de La RiojaGobierno de La RiojaMinisterio de Economía y Competitividad (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2020202020192020info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/202764reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/RYC-2015-18429http://dx.doi.org/10.3390/s19173799Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2027642026-05-22T06:33:51Z
dc.title.none.fl_str_mv A non-invasive method based on computer vision for grapevine cluster compactness assessment using a mobile sensing platform under field conditions
title A non-invasive method based on computer vision for grapevine cluster compactness assessment using a mobile sensing platform under field conditions
spellingShingle A non-invasive method based on computer vision for grapevine cluster compactness assessment using a mobile sensing platform under field conditions
Palacios Arribas, Fernando
Image analysis
Cluster morphology
RGB
Machine learning
Non-invasive sensing technologies
Proximal sensing
Precision viticulture
title_short A non-invasive method based on computer vision for grapevine cluster compactness assessment using a mobile sensing platform under field conditions
title_full A non-invasive method based on computer vision for grapevine cluster compactness assessment using a mobile sensing platform under field conditions
title_fullStr A non-invasive method based on computer vision for grapevine cluster compactness assessment using a mobile sensing platform under field conditions
title_full_unstemmed A non-invasive method based on computer vision for grapevine cluster compactness assessment using a mobile sensing platform under field conditions
title_sort A non-invasive method based on computer vision for grapevine cluster compactness assessment using a mobile sensing platform under field conditions
dc.creator.none.fl_str_mv Palacios Arribas, Fernando
Diago, Maria P.
Tardáguila, Javier
author Palacios Arribas, Fernando
author_facet Palacios Arribas, Fernando
Diago, Maria P.
Tardáguila, Javier
author_role author
author2 Diago, Maria P.
Tardáguila, Javier
author2_role author
author
dc.contributor.none.fl_str_mv Universidad de La Rioja
Gobierno de La Rioja
Ministerio de Economía y Competitividad (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Image analysis
Cluster morphology
RGB
Machine learning
Non-invasive sensing technologies
Proximal sensing
Precision viticulture
topic Image analysis
Cluster morphology
RGB
Machine learning
Non-invasive sensing technologies
Proximal sensing
Precision viticulture
description Grapevine cluster compactness affects grape composition, fungal disease incidence, and wine quality. Thus far, cluster compactness assessment has been based on visual inspection performed by trained evaluators with very scarce application in the wine industry. The goal of this work was to develop a new, non-invasive method based on the combination of computer vision and machine learning technology for cluster compactness assessment under field conditions from on-the-go red, green, blue (RGB) image acquisition. A mobile sensing platform was used to automatically capture RGB images of grapevine canopies and fruiting zones at night using artificial illumination. Likewise, a set of 195 clusters of four red grapevine varieties of three commercial vineyards were photographed during several years one week prior to harvest. After image acquisition, cluster compactness was evaluated by a group of 15 experts in the laboratory following the International Organization of Vine and Wine (OIV) 204 standard as a reference method. The developed algorithm comprises several steps, including an initial, semi-supervised image segmentation, followed by automated cluster detection and automated compactness estimation using a Gaussian process regression model. Calibration (95 clusters were used as a training set and 100 clusters as the test set) and leave-one-out cross-validation models (LOOCV; performed on the whole 195 clusters set) were elaborated. For these, determination coefficient (R2) of 0.68 and a root mean squared error (RMSE) of 0.96 were obtained on the test set between the image-based compactness estimated values and the average of the evaluators' ratings (in the range from 1-9). Additionally, the leave-one-out cross-validation yielded a R2 of 0.70 and an RMSE of 1.11. The results show that the newly developed computer vision based method could be commercially applied by the wine industry for efficient cluster compactness estimation from RGB on-the-go image acquisition platforms in commercial vineyards.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
2020
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/10261/202764
url http://hdl.handle.net/10261/202764
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/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/RYC-2015-18429
http://dx.doi.org/10.3390/s19173799
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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