Deep learning and computer vision for assessing the number of actual berries in commercial vineyards

The number of berries is one of the most relevant yield components that drives grape production in viticulture. The goal of this work was to estimate the number of actual berries per grapevine using computer vision and deep learning in commercial vineyards. Images from the visible range (RGB) were a...

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Autores: Palacios Arribas, Fernando, Melo-Pinto, Pedro, Diago, Maria P., Tardáguila, Javier
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/283615
Acceso en línea:http://hdl.handle.net/10261/283615
Access Level:acceso abierto
Palabra clave:Grapevine yield components
Non-invasive sensing technologies
Precision viticulture
SegNet architecture
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spelling Deep learning and computer vision for assessing the number of actual berries in commercial vineyardsPalacios Arribas, FernandoMelo-Pinto, PedroDiago, Maria P.Tardáguila, JavierGrapevine yield componentsNon-invasive sensing technologiesPrecision viticultureSegNet architectureThe number of berries is one of the most relevant yield components that drives grape production in viticulture. The goal of this work was to estimate the number of actual berries per grapevine using computer vision and deep learning in commercial vineyards. Images from the visible range (RGB) were acquired from a set of 96 grapevines (Vitis vinifera L.) at pea-size berry stage using a red, green and blue camera (RGB). At harvest, the number of berries and per vine was manually assessed as the ground-truth values. The algorithm involved computer vision to detect berries in the images and to extract canopy features, in order to gain information about canopy occlusion. These were used by the machine learning regression models built to estimate the number of actual berries per vine. A SegNet architecture was used to segment individual berries and several canopy related features. Four datasets were created combining the number of estimated visible berries and different canopy features. Three different regression models were tested on the four datasets. The best results were achieved with support vector regression (SVR) on a dataset including six canopy features. This method yielded a root mean squared error (RMSE) of 205 berries, a normalised root mean squared error (NRMSE) of 24.99% and a coefficient of determination (R) of 0.83 between the number of estimated and the number of actual berries per vine. The results show that the number of actual berries in grapevines can be assessed with high accuracy up to 60 days prior to grape harvest, using the developed algorithm based on computer vision and deep learning.Fernando Palacios would like to acknowledge the research founding FPI grant 286/2017 by Universidad de La Rioja, Gobierno de La Rioja. This work is supported by National Funds by FCT – Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020.Academic PressElsevierUniversidad de La RiojaGobierno de La RiojaFoundation for Science and TechnologyConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2022202220222022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/283615reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésThe underlying dataset has been published as supplementary material of the article in the publisher platform at 10.1016/j.biosystemseng.2022.04.015http://dx.doi.org/10.1016/j.biosystemseng.2022.04.015Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2836152026-05-22T06:33:51Z
dc.title.none.fl_str_mv Deep learning and computer vision for assessing the number of actual berries in commercial vineyards
title Deep learning and computer vision for assessing the number of actual berries in commercial vineyards
spellingShingle Deep learning and computer vision for assessing the number of actual berries in commercial vineyards
Palacios Arribas, Fernando
Grapevine yield components
Non-invasive sensing technologies
Precision viticulture
SegNet architecture
title_short Deep learning and computer vision for assessing the number of actual berries in commercial vineyards
title_full Deep learning and computer vision for assessing the number of actual berries in commercial vineyards
title_fullStr Deep learning and computer vision for assessing the number of actual berries in commercial vineyards
title_full_unstemmed Deep learning and computer vision for assessing the number of actual berries in commercial vineyards
title_sort Deep learning and computer vision for assessing the number of actual berries in commercial vineyards
dc.creator.none.fl_str_mv Palacios Arribas, Fernando
Melo-Pinto, Pedro
Diago, Maria P.
Tardáguila, Javier
author Palacios Arribas, Fernando
author_facet Palacios Arribas, Fernando
Melo-Pinto, Pedro
Diago, Maria P.
Tardáguila, Javier
author_role author
author2 Melo-Pinto, Pedro
Diago, Maria P.
Tardáguila, Javier
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidad de La Rioja
Gobierno de La Rioja
Foundation for Science and Technology
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Grapevine yield components
Non-invasive sensing technologies
Precision viticulture
SegNet architecture
topic Grapevine yield components
Non-invasive sensing technologies
Precision viticulture
SegNet architecture
description The number of berries is one of the most relevant yield components that drives grape production in viticulture. The goal of this work was to estimate the number of actual berries per grapevine using computer vision and deep learning in commercial vineyards. Images from the visible range (RGB) were acquired from a set of 96 grapevines (Vitis vinifera L.) at pea-size berry stage using a red, green and blue camera (RGB). At harvest, the number of berries and per vine was manually assessed as the ground-truth values. The algorithm involved computer vision to detect berries in the images and to extract canopy features, in order to gain information about canopy occlusion. These were used by the machine learning regression models built to estimate the number of actual berries per vine. A SegNet architecture was used to segment individual berries and several canopy related features. Four datasets were created combining the number of estimated visible berries and different canopy features. Three different regression models were tested on the four datasets. The best results were achieved with support vector regression (SVR) on a dataset including six canopy features. This method yielded a root mean squared error (RMSE) of 205 berries, a normalised root mean squared error (NRMSE) of 24.99% and a coefficient of determination (R) of 0.83 between the number of estimated and the number of actual berries per vine. The results show that the number of actual berries in grapevines can be assessed with high accuracy up to 60 days prior to grape harvest, using the developed algorithm based on computer vision and deep learning.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
2022
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/10261/283615
url http://hdl.handle.net/10261/283615
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The underlying dataset has been published as supplementary material of the article in the publisher platform at 10.1016/j.biosystemseng.2022.04.015
http://dx.doi.org/10.1016/j.biosystemseng.2022.04.015
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Academic Press
Elsevier
publisher.none.fl_str_mv Academic Press
Elsevier
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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