Seed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approach
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Agronomy (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/55960 |
Resumo: | Brachiaria brizantha is the most economically important forage grass in Brazil and is propagated through sowing. Producing high-quality seeds has been a constant challenge due to their uneven maturation. The development and application of precise and non-destructive methods for identifying internal damages to seeds, such as the X-ray test, which quickly indicates the quality of the lots, is of fundamental importance for the seed industry. In this work, the quality of Brachiaria brizantha seeds was analyzed based on the morphological characteristics observed in X-ray images that were related to viability using a mixture model under a Bayesian approach, with the following objectives: i) verify the adequacy of the Bayesian modeling used in the data analysis; ii) associate the efficiency of using radiographs as a way to assess the viability of the seeds; and iii) relate the classifications carried out by evaluators with the probability of originating normal or abnormal seedlings. The methodology applied for the analysis proved to be adequate. Further, the Bayesian estimates for parameters related to internal morphology were established with associated levels of uncertainty, which represents an advantage over usual frequentist methods. Based on the model's estimates, seeds evaluated as potentially unviable by three evaluators had practically no probability of germination and did not germinate in the test applied later. Seeds classified as potentially viable had a high probability of developing into normal seedlings, while 73.27% showed this property in the germination test. |
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Acta Scientiarum. Agronomy (Online) |
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Seed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approachSeed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approachmixture model; Bayesian inference; X-ray test.mixture model; Bayesian inference; X-ray test.Brachiaria brizantha is the most economically important forage grass in Brazil and is propagated through sowing. Producing high-quality seeds has been a constant challenge due to their uneven maturation. The development and application of precise and non-destructive methods for identifying internal damages to seeds, such as the X-ray test, which quickly indicates the quality of the lots, is of fundamental importance for the seed industry. In this work, the quality of Brachiaria brizantha seeds was analyzed based on the morphological characteristics observed in X-ray images that were related to viability using a mixture model under a Bayesian approach, with the following objectives: i) verify the adequacy of the Bayesian modeling used in the data analysis; ii) associate the efficiency of using radiographs as a way to assess the viability of the seeds; and iii) relate the classifications carried out by evaluators with the probability of originating normal or abnormal seedlings. The methodology applied for the analysis proved to be adequate. Further, the Bayesian estimates for parameters related to internal morphology were established with associated levels of uncertainty, which represents an advantage over usual frequentist methods. Based on the model's estimates, seeds evaluated as potentially unviable by three evaluators had practically no probability of germination and did not germinate in the test applied later. Seeds classified as potentially viable had a high probability of developing into normal seedlings, while 73.27% showed this property in the germination test.Brachiaria brizantha is the most economically important forage grass in Brazil and is propagated through sowing. Producing high-quality seeds has been a constant challenge due to their uneven maturation. The development and application of precise and non-destructive methods for identifying internal damages to seeds, such as the X-ray test, which quickly indicates the quality of the lots, is of fundamental importance for the seed industry. In this work, the quality of Brachiaria brizantha seeds was analyzed based on the morphological characteristics observed in X-ray images that were related to viability using a mixture model under a Bayesian approach, with the following objectives: i) verify the adequacy of the Bayesian modeling used in the data analysis; ii) associate the efficiency of using radiographs as a way to assess the viability of the seeds; and iii) relate the classifications carried out by evaluators with the probability of originating normal or abnormal seedlings. The methodology applied for the analysis proved to be adequate. Further, the Bayesian estimates for parameters related to internal morphology were established with associated levels of uncertainty, which represents an advantage over usual frequentist methods. Based on the model's estimates, seeds evaluated as potentially unviable by three evaluators had practically no probability of germination and did not germinate in the test applied later. Seeds classified as potentially viable had a high probability of developing into normal seedlings, while 73.27% showed this property in the germination test.Universidade Estadual de Maringá2022-06-29info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/5596010.4025/actasciagron.v44i1.55960Acta Scientiarum. Agronomy; Vol 44 (2022): Publicação contínua; e55960Acta Scientiarum. Agronomy; v. 44 (2022): Publicação contínua; e559601807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/55960/751375154484Copyright (c) 2022 Acta Scientiarum. Agronomyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess Silva, Alessandra Querino daOliveira, Luciano Antonio de Silva, Carlos Pereira da Mendes, Cristian Tiago ErazoFerreira, Ana Maria Oliveira Sáfadi, ThelmaCarvalho, Maria Laene Moreira de 2022-07-28T14:25:31Zoai:periodicos.uem.br/ojs:article/55960Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2022-07-28T14:25:31Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Seed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approach Seed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approach |
title |
Seed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approach |
spellingShingle |
Seed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approach Silva, Alessandra Querino da mixture model; Bayesian inference; X-ray test. mixture model; Bayesian inference; X-ray test. |
title_short |
Seed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approach |
title_full |
Seed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approach |
title_fullStr |
Seed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approach |
title_full_unstemmed |
Seed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approach |
title_sort |
Seed quality of Brachiaria brizantha by X-ray image analysis using a Bayesian approach |
author |
Silva, Alessandra Querino da |
author_facet |
Silva, Alessandra Querino da Oliveira, Luciano Antonio de Silva, Carlos Pereira da Mendes, Cristian Tiago Erazo Ferreira, Ana Maria Oliveira Sáfadi, Thelma Carvalho, Maria Laene Moreira de |
author_role |
author |
author2 |
Oliveira, Luciano Antonio de Silva, Carlos Pereira da Mendes, Cristian Tiago Erazo Ferreira, Ana Maria Oliveira Sáfadi, Thelma Carvalho, Maria Laene Moreira de |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Silva, Alessandra Querino da Oliveira, Luciano Antonio de Silva, Carlos Pereira da Mendes, Cristian Tiago Erazo Ferreira, Ana Maria Oliveira Sáfadi, Thelma Carvalho, Maria Laene Moreira de |
dc.subject.por.fl_str_mv |
mixture model; Bayesian inference; X-ray test. mixture model; Bayesian inference; X-ray test. |
topic |
mixture model; Bayesian inference; X-ray test. mixture model; Bayesian inference; X-ray test. |
description |
Brachiaria brizantha is the most economically important forage grass in Brazil and is propagated through sowing. Producing high-quality seeds has been a constant challenge due to their uneven maturation. The development and application of precise and non-destructive methods for identifying internal damages to seeds, such as the X-ray test, which quickly indicates the quality of the lots, is of fundamental importance for the seed industry. In this work, the quality of Brachiaria brizantha seeds was analyzed based on the morphological characteristics observed in X-ray images that were related to viability using a mixture model under a Bayesian approach, with the following objectives: i) verify the adequacy of the Bayesian modeling used in the data analysis; ii) associate the efficiency of using radiographs as a way to assess the viability of the seeds; and iii) relate the classifications carried out by evaluators with the probability of originating normal or abnormal seedlings. The methodology applied for the analysis proved to be adequate. Further, the Bayesian estimates for parameters related to internal morphology were established with associated levels of uncertainty, which represents an advantage over usual frequentist methods. Based on the model's estimates, seeds evaluated as potentially unviable by three evaluators had practically no probability of germination and did not germinate in the test applied later. Seeds classified as potentially viable had a high probability of developing into normal seedlings, while 73.27% showed this property in the germination test. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-06-29 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/55960 10.4025/actasciagron.v44i1.55960 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/55960 |
identifier_str_mv |
10.4025/actasciagron.v44i1.55960 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/55960/751375154484 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2022 Acta Scientiarum. Agronomy https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2022 Acta Scientiarum. Agronomy https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Agronomy; Vol 44 (2022): Publicação contínua; e55960 Acta Scientiarum. Agronomy; v. 44 (2022): Publicação contínua; e55960 1807-8621 1679-9275 reponame:Acta Scientiarum. Agronomy (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta Scientiarum. Agronomy (Online) |
collection |
Acta Scientiarum. Agronomy (Online) |
repository.name.fl_str_mv |
Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br |
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1799305911938842624 |