Low-Cost Three-Dimensional Modeling of Crop Plants

Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tes...

ver descrição completa

Detalhes bibliográficos
Autores: Martínez Guanter, Jorge, Ribeiro, Ángela, Peteinatos, Gerassimos G., Pérez Ruiz, Manuel, Gerhards, Roland, Bengoechea Guevara, José María, Machleb, Jannis, Andújar, Dionisio
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/89181
Acesso em linha:https://hdl.handle.net/11441/89181
https://doi.org/10.3390 / s19132883
Access Level:acceso abierto
Palavra-chave:Plant phenotyping
RGB-D
Structure from Motion
id ES_5d3f82b147cd08f885fd5358ae7faeed
oai_identifier_str oai:idus.us.es:11441/89181
network_acronym_str ES
network_name_str España
repository_id_str
spelling Low-Cost Three-Dimensional Modeling of Crop PlantsMartínez Guanter, JorgeRibeiro, ÁngelaPeteinatos, Gerassimos G.Pérez Ruiz, ManuelGerhards, RolandBengoechea Guevara, José MaríaMachleb, JannisAndújar, DionisioPlant phenotypingRGB-DStructure from MotionPlant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.MDPIIngeniería Aeroespacial y Mecánica de Fluidos2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlapplication/pdfhttps://hdl.handle.net/11441/89181https://doi.org/10.3390 / s19132883reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésSensors, 19 (13), 2883-1-2883-14.https://doi.org/10.3390/s19132883info:eu-repo/semantics/openAccessoai:idus.us.es:11441/891812026-06-17T12:51:07Z
dc.title.none.fl_str_mv Low-Cost Three-Dimensional Modeling of Crop Plants
title Low-Cost Three-Dimensional Modeling of Crop Plants
spellingShingle Low-Cost Three-Dimensional Modeling of Crop Plants
Martínez Guanter, Jorge
Plant phenotyping
RGB-D
Structure from Motion
title_short Low-Cost Three-Dimensional Modeling of Crop Plants
title_full Low-Cost Three-Dimensional Modeling of Crop Plants
title_fullStr Low-Cost Three-Dimensional Modeling of Crop Plants
title_full_unstemmed Low-Cost Three-Dimensional Modeling of Crop Plants
title_sort Low-Cost Three-Dimensional Modeling of Crop Plants
dc.creator.none.fl_str_mv Martínez Guanter, Jorge
Ribeiro, Ángela
Peteinatos, Gerassimos G.
Pérez Ruiz, Manuel
Gerhards, Roland
Bengoechea Guevara, José María
Machleb, Jannis
Andújar, Dionisio
author Martínez Guanter, Jorge
author_facet Martínez Guanter, Jorge
Ribeiro, Ángela
Peteinatos, Gerassimos G.
Pérez Ruiz, Manuel
Gerhards, Roland
Bengoechea Guevara, José María
Machleb, Jannis
Andújar, Dionisio
author_role author
author2 Ribeiro, Ángela
Peteinatos, Gerassimos G.
Pérez Ruiz, Manuel
Gerhards, Roland
Bengoechea Guevara, José María
Machleb, Jannis
Andújar, Dionisio
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería Aeroespacial y Mecánica de Fluidos
dc.subject.none.fl_str_mv Plant phenotyping
RGB-D
Structure from Motion
topic Plant phenotyping
RGB-D
Structure from Motion
description Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.
publishDate 2019
dc.date.none.fl_str_mv 2019
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/11441/89181
https://doi.org/10.3390 / s19132883
url https://hdl.handle.net/11441/89181
https://doi.org/10.3390 / s19132883
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sensors, 19 (13), 2883-1-2883-14.
https://doi.org/10.3390/s19132883
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869409000362082304
score 15,300719

Registos relacionados