The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping

Detalhes bibliográficos
Autor(a) principal: Souza, Jardel da Silva [UNESP]
Data de Publicação: 2022
Outros Autores: Pedrosa, Laura Monteiro, Moreira, Bruno Rafael de Almeida [UNESP], Rêgo, Elizanilda Ramalho do, Unêda-Trevisoli, Sandra Helena [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.3390/agronomy12061342
http://hdl.handle.net/11449/240255
Resumo: A breeder can select a visually appealing phenotype, whether for ornamentation or land-scaping. However, the organic vision is not accurate and objective, making it challenging to bring a reliable phenotyping intervention into implementation. Therefore, the objective of this study was to develop an innovative solution to predict the intensity of the flower’s color upon the external shape of the crop. We merged the single linear iterative clustering (SLIC) algorithm and box-counting method (BCM) into a framework to extract useful imagery data for biophysical modeling. Then, we validated our approach by fitting Gompertz function to data on intensity of flower’s color and fractal dimension (SD) of the architecture of white-flower, yellow-flower, and red-flower varieties of Portulaca umbraticola. The SLIC algorithm segmented the images into uniform superpixels, enabling the BCM to precisely capture the SD of the architecture. The SD ranged from 1.938315 to 1.941630, which corresponded to pixel-wise intensities of 220.85 and 47.15. Thus, the more compact the architecture the more intensive the color of the flower. The sigmoid Gompertz function predicted such a relationship at radj2 > 0.80. This study can provide further knowledge to progress the field’s prominence in developing breakthrough strategies toward improving the control of visual quality and breeding of ornamentals.
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spelling The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotypingbox-counting methodfractal geometry theoryimagery processingPortulaca umbraticolasuperpixel segmentationA breeder can select a visually appealing phenotype, whether for ornamentation or land-scaping. However, the organic vision is not accurate and objective, making it challenging to bring a reliable phenotyping intervention into implementation. Therefore, the objective of this study was to develop an innovative solution to predict the intensity of the flower’s color upon the external shape of the crop. We merged the single linear iterative clustering (SLIC) algorithm and box-counting method (BCM) into a framework to extract useful imagery data for biophysical modeling. Then, we validated our approach by fitting Gompertz function to data on intensity of flower’s color and fractal dimension (SD) of the architecture of white-flower, yellow-flower, and red-flower varieties of Portulaca umbraticola. The SLIC algorithm segmented the images into uniform superpixels, enabling the BCM to precisely capture the SD of the architecture. The SD ranged from 1.938315 to 1.941630, which corresponded to pixel-wise intensities of 220.85 and 47.15. Thus, the more compact the architecture the more intensive the color of the flower. The sigmoid Gompertz function predicted such a relationship at radj2 > 0.80. This study can provide further knowledge to progress the field’s prominence in developing breakthrough strategies toward improving the control of visual quality and breeding of ornamentals.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)School of Agricultural and Veterinary Sciences São Paulo State University (Unesp), SPCenter for Agricultural Sciences Federal University of Paraíba (UFPB), PBSchool of Agricultural and Veterinary Sciences São Paulo State University (Unesp), SPCNPq: 442104/2019-7Universidade Estadual Paulista (UNESP)Universidade Federal da Paraíba (UFPB)Souza, Jardel da Silva [UNESP]Pedrosa, Laura MonteiroMoreira, Bruno Rafael de Almeida [UNESP]Rêgo, Elizanilda Ramalho doUnêda-Trevisoli, Sandra Helena [UNESP]2023-03-01T20:08:39Z2023-03-01T20:08:39Z2022-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/agronomy12061342Agronomy, v. 12, n. 6, 2022.2073-4395http://hdl.handle.net/11449/24025510.3390/agronomy120613422-s2.0-85132007126Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAgronomyinfo:eu-repo/semantics/openAccess2023-03-01T20:08:39Zoai:repositorio.unesp.br:11449/240255Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-03-01T20:08:39Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping
title The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping
spellingShingle The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping
Souza, Jardel da Silva [UNESP]
box-counting method
fractal geometry theory
imagery processing
Portulaca umbraticola
superpixel segmentation
title_short The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping
title_full The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping
title_fullStr The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping
title_full_unstemmed The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping
title_sort The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping
author Souza, Jardel da Silva [UNESP]
author_facet Souza, Jardel da Silva [UNESP]
Pedrosa, Laura Monteiro
Moreira, Bruno Rafael de Almeida [UNESP]
Rêgo, Elizanilda Ramalho do
Unêda-Trevisoli, Sandra Helena [UNESP]
author_role author
author2 Pedrosa, Laura Monteiro
Moreira, Bruno Rafael de Almeida [UNESP]
Rêgo, Elizanilda Ramalho do
Unêda-Trevisoli, Sandra Helena [UNESP]
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Universidade Federal da Paraíba (UFPB)
dc.contributor.author.fl_str_mv Souza, Jardel da Silva [UNESP]
Pedrosa, Laura Monteiro
Moreira, Bruno Rafael de Almeida [UNESP]
Rêgo, Elizanilda Ramalho do
Unêda-Trevisoli, Sandra Helena [UNESP]
dc.subject.por.fl_str_mv box-counting method
fractal geometry theory
imagery processing
Portulaca umbraticola
superpixel segmentation
topic box-counting method
fractal geometry theory
imagery processing
Portulaca umbraticola
superpixel segmentation
description A breeder can select a visually appealing phenotype, whether for ornamentation or land-scaping. However, the organic vision is not accurate and objective, making it challenging to bring a reliable phenotyping intervention into implementation. Therefore, the objective of this study was to develop an innovative solution to predict the intensity of the flower’s color upon the external shape of the crop. We merged the single linear iterative clustering (SLIC) algorithm and box-counting method (BCM) into a framework to extract useful imagery data for biophysical modeling. Then, we validated our approach by fitting Gompertz function to data on intensity of flower’s color and fractal dimension (SD) of the architecture of white-flower, yellow-flower, and red-flower varieties of Portulaca umbraticola. The SLIC algorithm segmented the images into uniform superpixels, enabling the BCM to precisely capture the SD of the architecture. The SD ranged from 1.938315 to 1.941630, which corresponded to pixel-wise intensities of 220.85 and 47.15. Thus, the more compact the architecture the more intensive the color of the flower. The sigmoid Gompertz function predicted such a relationship at radj2 > 0.80. This study can provide further knowledge to progress the field’s prominence in developing breakthrough strategies toward improving the control of visual quality and breeding of ornamentals.
publishDate 2022
dc.date.none.fl_str_mv 2022-06-01
2023-03-01T20:08:39Z
2023-03-01T20:08:39Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.3390/agronomy12061342
Agronomy, v. 12, n. 6, 2022.
2073-4395
http://hdl.handle.net/11449/240255
10.3390/agronomy12061342
2-s2.0-85132007126
url http://dx.doi.org/10.3390/agronomy12061342
http://hdl.handle.net/11449/240255
identifier_str_mv Agronomy, v. 12, n. 6, 2022.
2073-4395
10.3390/agronomy12061342
2-s2.0-85132007126
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Agronomy
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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