A stomata classification and detection system in microscope images of maize cultivars
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1371/journal.pone.0258679 http://hdl.handle.net/11449/229790 |
Resumo: | Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural net-woks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses. |
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A stomata classification and detection system in microscope images of maize cultivarsPlant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural net-woks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Instituto de Ciência e Tecnologia Universidade Federal de São Paulo São José dos CamposInstituto de Biologia Universidade Federal de Uberlândia, UberlândiaDepartment of Computing São Paulo State University, BauruDepartment of Computing São Paulo State University, BauruCNPq: 2018/23908-1CNPq: 307066/2017-7CNPq: 408919/2016-7Universidade Federal de São Paulo (UNIFESP)Universidade Federal de Uberlândia (UFU)Universidade Estadual Paulista (UNESP)Aono, Alexandre H.Nagai, James S.Dickel, Gabriella da S.M.Marinho, Rafaela C.de Oliveira, Paulo E.A.M.Papa, João P. [UNESP]Faria, Fabio A.2022-04-29T08:35:46Z2022-04-29T08:35:46Z2021-10-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1371/journal.pone.0258679PLoS ONE, v. 16, n. 10 October, 2021.1932-6203http://hdl.handle.net/11449/22979010.1371/journal.pone.02586792-s2.0-85117935727Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPLoS ONEinfo:eu-repo/semantics/openAccess2024-04-23T16:10:49Zoai:repositorio.unesp.br:11449/229790Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:10:49Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A stomata classification and detection system in microscope images of maize cultivars |
title |
A stomata classification and detection system in microscope images of maize cultivars |
spellingShingle |
A stomata classification and detection system in microscope images of maize cultivars Aono, Alexandre H. |
title_short |
A stomata classification and detection system in microscope images of maize cultivars |
title_full |
A stomata classification and detection system in microscope images of maize cultivars |
title_fullStr |
A stomata classification and detection system in microscope images of maize cultivars |
title_full_unstemmed |
A stomata classification and detection system in microscope images of maize cultivars |
title_sort |
A stomata classification and detection system in microscope images of maize cultivars |
author |
Aono, Alexandre H. |
author_facet |
Aono, Alexandre H. Nagai, James S. Dickel, Gabriella da S.M. Marinho, Rafaela C. de Oliveira, Paulo E.A.M. Papa, João P. [UNESP] Faria, Fabio A. |
author_role |
author |
author2 |
Nagai, James S. Dickel, Gabriella da S.M. Marinho, Rafaela C. de Oliveira, Paulo E.A.M. Papa, João P. [UNESP] Faria, Fabio A. |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Paulo (UNIFESP) Universidade Federal de Uberlândia (UFU) Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Aono, Alexandre H. Nagai, James S. Dickel, Gabriella da S.M. Marinho, Rafaela C. de Oliveira, Paulo E.A.M. Papa, João P. [UNESP] Faria, Fabio A. |
description |
Plant stomata are essential structures (pores) that control the exchange of gases between plant leaves and the atmosphere, and also they influence plant adaptation to climate through photosynthesis and transpiration stream. Many works in literature aim for a better understanding of these structures and their role in the evolution process and the behavior of plants. Although stomata studies in dicots species have advanced considerably in the past years, even there is not much knowledge about the stomata of cereal grasses. Due to the high morphological variation of stomata traits intra- and inter-species, detecting and classifying stomata automatically becomes challenging. For this reason, in this work, we propose a new system for automatic stomata classification and detection in microscope images for maize cultivars based on transfer learning strategy of different deep convolution neural net-woks (DCNN). Our performed experiments show that our system achieves an approximated accuracy of 97.1% in identifying stomata regions using classifiers based on deep learning features, which figures out as a nearly perfect classification system. As the stomata are responsible for several plant functionalities, this work represents an important advance for maize research, providing an accurate system in replacing the current manual task of categorizing these pores on microscope images. Furthermore, this system can also be a reference for studies using images from different cereal grasses. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10-01 2022-04-29T08:35:46Z 2022-04-29T08:35:46Z |
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/10.1371/journal.pone.0258679 PLoS ONE, v. 16, n. 10 October, 2021. 1932-6203 http://hdl.handle.net/11449/229790 10.1371/journal.pone.0258679 2-s2.0-85117935727 |
url |
http://dx.doi.org/10.1371/journal.pone.0258679 http://hdl.handle.net/11449/229790 |
identifier_str_mv |
PLoS ONE, v. 16, n. 10 October, 2021. 1932-6203 10.1371/journal.pone.0258679 2-s2.0-85117935727 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
PLoS ONE |
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_ |
1799965037535690752 |