Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of t...

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Autores: Rossi, Riccardo, Leolini, Claudio, Costafreda Aumedes, Sergi, Leolini, Luisa, Bindi, Marco, Zaldei, Alessandro, Moriondo, Marco
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/69820
Acceso en línea:https://doi.org/10.3390/s20113150
http://hdl.handle.net/10459.1/69820
Access Level:acceso abierto
Palabra clave:3D phenotyping
Low-cost platform
Plant imaging
Structure for motion
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spelling Performances Evaluation of a Low-Cost Platform for High-Resolution Plant PhenotypingRossi, RiccardoLeolini, ClaudioCostafreda Aumedes, SergiLeolini, LuisaBindi, MarcoZaldei, AlessandroMoriondo, Marco3D phenotypingLow-cost platformPlant imagingStructure for motionThis study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.MDPI202020202020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://doi.org/10.3390/s20113150http://hdl.handle.net/10459.1/69820http://hdl.handle.net/10459.1/69820reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.3390/s20113150Sensors, 2020, vol. 20, núm. 11, p. 3150cc-by (c) Rossi, Riccardo et al., 2020info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/oai:recercat.cat:10459.1/698202026-05-29T05:05:01Z
dc.title.none.fl_str_mv Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping
title Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping
spellingShingle Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping
Rossi, Riccardo
3D phenotyping
Low-cost platform
Plant imaging
Structure for motion
title_short Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping
title_full Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping
title_fullStr Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping
title_full_unstemmed Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping
title_sort Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping
dc.creator.none.fl_str_mv Rossi, Riccardo
Leolini, Claudio
Costafreda Aumedes, Sergi
Leolini, Luisa
Bindi, Marco
Zaldei, Alessandro
Moriondo, Marco
author Rossi, Riccardo
author_facet Rossi, Riccardo
Leolini, Claudio
Costafreda Aumedes, Sergi
Leolini, Luisa
Bindi, Marco
Zaldei, Alessandro
Moriondo, Marco
author_role author
author2 Leolini, Claudio
Costafreda Aumedes, Sergi
Leolini, Luisa
Bindi, Marco
Zaldei, Alessandro
Moriondo, Marco
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv 3D phenotyping
Low-cost platform
Plant imaging
Structure for motion
topic 3D phenotyping
Low-cost platform
Plant imaging
Structure for motion
description This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
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://doi.org/10.3390/s20113150
http://hdl.handle.net/10459.1/69820
http://hdl.handle.net/10459.1/69820
url https://doi.org/10.3390/s20113150
http://hdl.handle.net/10459.1/69820
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/s20113150
Sensors, 2020, vol. 20, núm. 11, p. 3150
dc.rights.none.fl_str_mv cc-by (c) Rossi, Riccardo et al., 2020
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
rights_invalid_str_mv cc-by (c) Rossi, Riccardo et al., 2020
http://creativecommons.org/licenses/by/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:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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