The More Fractal the Architecture the More Intensive the Color of Flower: A Superpixel-Wise Analysis towards High-Throughput Phenotyping
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
Outros Autores: | , , , |
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. |
id |
UNSP_3b65e69ac241712fef861d44f66cfc85 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/240255 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
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 |
|
_version_ |
1799964667293990912 |