Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception

Autores
Moreno, Hugo; Rueda Ayala, Victor; Ribeiro, Angela; Bengochea Guevara, Jose; López Correa, Juan Manuel; Peteinatos, Gerassimos; Valero, Constantino; Andújar, Dionisio
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A non-destructive measuring technique was applied to test major vine geometric traits on measurements collected by a contactless sensor. Three-dimensional optical sensors have evolved over the past decade, and these advancements may be useful in improving phenomics technologies for other crops, such as woody perennials. Red, green and blue-depth (RGB-D) cameras, namely Microsoft Kinect, have a significant influence on recent computer vision and robotics research. In this experiment an adaptable mobile platform was used for the acquisition of depth images for the non-destructive assessment of branch volume (pruning weight) and related to grape yield in vineyard crops. Vineyard yield prediction provides useful insights about the anticipated yield to the winegrower, guiding strategic decisions to accomplish optimal quantity and efficiency, and supporting the winegrower with decision-making. A Kinect v2 system on-board to an on-ground electric vehicle was capable of producing precise 3D point clouds of vine rows under six different management cropping systems. The generated models demonstrated strong consistency between 3D images and vine structures from the actual physical parameters when average values were calculated. Correlations of Kinect branch volume with pruning weight (dry biomass) resulted in high coefficients of determination (R2 = 0.80). In the study of vineyard yield correlations, the measured volume was found to have a good power law relationship (R2 = 0.87). However due to low capability of most depth cameras to properly build 3-D shapes of small details the results for each treatment when calculated separately were not consistent. Nonetheless, Kinect v2 has a tremendous potential as a 3D sensor in agricultural applications for proximal sensing operations, benefiting from its high frame rate, low price in comparison with other depth cameras, and high robustness.
Fil: Moreno, Hugo. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; España
Fil: Rueda Ayala, Victor. Norwegian Institute of Bioeconomy Research; Noruega
Fil: Ribeiro, Angela. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; España
Fil: Bengochea Guevara, Jose. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; España
Fil: López Correa, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Politécnica de Madrid; España
Fil: Peteinatos, Gerassimos. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; España
Fil: Valero, Constantino. Universidad Politécnica de Madrid; España
Fil: Andújar, Dionisio. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; España
Materia
depth cameras
Kinect v2
3D reconstruction
vineyards
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/256987
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/256987
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth PerceptionMoreno, HugoRueda Ayala, VictorRibeiro, AngelaBengochea Guevara, JoseLópez Correa, Juan ManuelPeteinatos, GerassimosValero, ConstantinoAndújar, Dionisiodepth camerasKinect v23D reconstructionvineyardshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1A non-destructive measuring technique was applied to test major vine geometric traits on measurements collected by a contactless sensor. Three-dimensional optical sensors have evolved over the past decade, and these advancements may be useful in improving phenomics technologies for other crops, such as woody perennials. Red, green and blue-depth (RGB-D) cameras, namely Microsoft Kinect, have a significant influence on recent computer vision and robotics research. In this experiment an adaptable mobile platform was used for the acquisition of depth images for the non-destructive assessment of branch volume (pruning weight) and related to grape yield in vineyard crops. Vineyard yield prediction provides useful insights about the anticipated yield to the winegrower, guiding strategic decisions to accomplish optimal quantity and efficiency, and supporting the winegrower with decision-making. A Kinect v2 system on-board to an on-ground electric vehicle was capable of producing precise 3D point clouds of vine rows under six different management cropping systems. The generated models demonstrated strong consistency between 3D images and vine structures from the actual physical parameters when average values were calculated. Correlations of Kinect branch volume with pruning weight (dry biomass) resulted in high coefficients of determination (R2 = 0.80). In the study of vineyard yield correlations, the measured volume was found to have a good power law relationship (R2 = 0.87). However due to low capability of most depth cameras to properly build 3-D shapes of small details the results for each treatment when calculated separately were not consistent. Nonetheless, Kinect v2 has a tremendous potential as a 3D sensor in agricultural applications for proximal sensing operations, benefiting from its high frame rate, low price in comparison with other depth cameras, and high robustness.Fil: Moreno, Hugo. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; EspañaFil: Rueda Ayala, Victor. Norwegian Institute of Bioeconomy Research; NoruegaFil: Ribeiro, Angela. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; EspañaFil: Bengochea Guevara, Jose. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; EspañaFil: López Correa, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Politécnica de Madrid; EspañaFil: Peteinatos, Gerassimos. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; EspañaFil: Valero, Constantino. Universidad Politécnica de Madrid; EspañaFil: Andújar, Dionisio. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; EspañaMolecular Diversity Preservation International2020-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/256987Moreno, Hugo; Rueda Ayala, Victor; Ribeiro, Angela; Bengochea Guevara, Jose; López Correa, Juan Manuel; et al.; Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception; Molecular Diversity Preservation International; Sensors; 20; 23; 12-2020; 1-141424-8220CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1424-8220/20/23/6912info:eu-repo/semantics/altIdentifier/doi/10.3390/s20236912info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:40:56Zoai:ri.conicet.gov.ar:11336/256987instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:40:56.955CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception
title Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception
spellingShingle Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception
Moreno, Hugo
depth cameras
Kinect v2
3D reconstruction
vineyards
title_short Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception
title_full Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception
title_fullStr Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception
title_full_unstemmed Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception
title_sort Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception
dc.creator.none.fl_str_mv Moreno, Hugo
Rueda Ayala, Victor
Ribeiro, Angela
Bengochea Guevara, Jose
López Correa, Juan Manuel
Peteinatos, Gerassimos
Valero, Constantino
Andújar, Dionisio
author Moreno, Hugo
author_facet Moreno, Hugo
Rueda Ayala, Victor
Ribeiro, Angela
Bengochea Guevara, Jose
López Correa, Juan Manuel
Peteinatos, Gerassimos
Valero, Constantino
Andújar, Dionisio
author_role author
author2 Rueda Ayala, Victor
Ribeiro, Angela
Bengochea Guevara, Jose
López Correa, Juan Manuel
Peteinatos, Gerassimos
Valero, Constantino
Andújar, Dionisio
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv depth cameras
Kinect v2
3D reconstruction
vineyards
topic depth cameras
Kinect v2
3D reconstruction
vineyards
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A non-destructive measuring technique was applied to test major vine geometric traits on measurements collected by a contactless sensor. Three-dimensional optical sensors have evolved over the past decade, and these advancements may be useful in improving phenomics technologies for other crops, such as woody perennials. Red, green and blue-depth (RGB-D) cameras, namely Microsoft Kinect, have a significant influence on recent computer vision and robotics research. In this experiment an adaptable mobile platform was used for the acquisition of depth images for the non-destructive assessment of branch volume (pruning weight) and related to grape yield in vineyard crops. Vineyard yield prediction provides useful insights about the anticipated yield to the winegrower, guiding strategic decisions to accomplish optimal quantity and efficiency, and supporting the winegrower with decision-making. A Kinect v2 system on-board to an on-ground electric vehicle was capable of producing precise 3D point clouds of vine rows under six different management cropping systems. The generated models demonstrated strong consistency between 3D images and vine structures from the actual physical parameters when average values were calculated. Correlations of Kinect branch volume with pruning weight (dry biomass) resulted in high coefficients of determination (R2 = 0.80). In the study of vineyard yield correlations, the measured volume was found to have a good power law relationship (R2 = 0.87). However due to low capability of most depth cameras to properly build 3-D shapes of small details the results for each treatment when calculated separately were not consistent. Nonetheless, Kinect v2 has a tremendous potential as a 3D sensor in agricultural applications for proximal sensing operations, benefiting from its high frame rate, low price in comparison with other depth cameras, and high robustness.
Fil: Moreno, Hugo. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; España
Fil: Rueda Ayala, Victor. Norwegian Institute of Bioeconomy Research; Noruega
Fil: Ribeiro, Angela. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; España
Fil: Bengochea Guevara, Jose. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; España
Fil: López Correa, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Politécnica de Madrid; España
Fil: Peteinatos, Gerassimos. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; España
Fil: Valero, Constantino. Universidad Politécnica de Madrid; España
Fil: Andújar, Dionisio. Consejo Superior de Investigaciones Científicas; España. Universidad Politécnica de Madrid; España
description A non-destructive measuring technique was applied to test major vine geometric traits on measurements collected by a contactless sensor. Three-dimensional optical sensors have evolved over the past decade, and these advancements may be useful in improving phenomics technologies for other crops, such as woody perennials. Red, green and blue-depth (RGB-D) cameras, namely Microsoft Kinect, have a significant influence on recent computer vision and robotics research. In this experiment an adaptable mobile platform was used for the acquisition of depth images for the non-destructive assessment of branch volume (pruning weight) and related to grape yield in vineyard crops. Vineyard yield prediction provides useful insights about the anticipated yield to the winegrower, guiding strategic decisions to accomplish optimal quantity and efficiency, and supporting the winegrower with decision-making. A Kinect v2 system on-board to an on-ground electric vehicle was capable of producing precise 3D point clouds of vine rows under six different management cropping systems. The generated models demonstrated strong consistency between 3D images and vine structures from the actual physical parameters when average values were calculated. Correlations of Kinect branch volume with pruning weight (dry biomass) resulted in high coefficients of determination (R2 = 0.80). In the study of vineyard yield correlations, the measured volume was found to have a good power law relationship (R2 = 0.87). However due to low capability of most depth cameras to properly build 3-D shapes of small details the results for each treatment when calculated separately were not consistent. Nonetheless, Kinect v2 has a tremendous potential as a 3D sensor in agricultural applications for proximal sensing operations, benefiting from its high frame rate, low price in comparison with other depth cameras, and high robustness.
publishDate 2020
dc.date.none.fl_str_mv 2020-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/256987
Moreno, Hugo; Rueda Ayala, Victor; Ribeiro, Angela; Bengochea Guevara, Jose; López Correa, Juan Manuel; et al.; Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception; Molecular Diversity Preservation International; Sensors; 20; 23; 12-2020; 1-14
1424-8220
CONICET Digital
CONICET
url http://hdl.handle.net/11336/256987
identifier_str_mv Moreno, Hugo; Rueda Ayala, Victor; Ribeiro, Angela; Bengochea Guevara, Jose; López Correa, Juan Manuel; et al.; Evaluation of Vineyard Cropping Systems Using On-Board RGB-Depth Perception; Molecular Diversity Preservation International; Sensors; 20; 23; 12-2020; 1-14
1424-8220
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1424-8220/20/23/6912
info:eu-repo/semantics/altIdentifier/doi/10.3390/s20236912
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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