Using rapid chlorophyll fluorescence transients to classify vitis genotypes
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/93403 |
Resumo: | Silva, J. M. da, Figueiredo, A., Cunha, J., Eiras-Dias, J. E., Silva, S., Vanneschi, L., & Mariano, P. (2020). Using rapid chlorophyll fluorescence transients to classify vitis genotypes. Plants, 9(2), 1-19. [174]. https://doi.org/10.3390/plants9020174 |
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Using rapid chlorophyll fluorescence transients to classify vitis genotypesArtificial neural networksChlorophyll a fluorescenceDecision treesGenetic programmingK-nearest neighborsKautsky effectMolecular markersPhotosynthesisVitisEcology, Evolution, Behavior and SystematicsEcologyPlant ScienceSDG 15 - Life on LandSilva, J. M. da, Figueiredo, A., Cunha, J., Eiras-Dias, J. E., Silva, S., Vanneschi, L., & Mariano, P. (2020). Using rapid chlorophyll fluorescence transients to classify vitis genotypes. Plants, 9(2), 1-19. [174]. https://doi.org/10.3390/plants9020174When a dark-adapted leaf is illuminated with saturating light, a fast polyphasic rise of fluorescence emission (Kautsky effect) is observed. The shape of the curve is dependent on the molecular organization of the photochemical apparatus, which in turn is a function of the interaction between genotype and environment. In this paper, we evaluate the potential of rapid fluorescence transients, aided by machine learning techniques, to classify plant genotypes. We present results of the application of several machine learning algorithms (k-nearest neighbors, decision trees, artificial neural networks, genetic programming) to rapid induction curves recorded in different species and cultivars of vine grown in the same environmental conditions. The phylogenetic relations between the selected Vitis species and Vitis vinifera cultivars were established with molecular markers. Both neural networks (71.8%) and genetic programming (75.3%) presented much higher global classification success rates than k-nearest neighbors (58.5%) or decision trees (51.6%), genetic programming performing slightly better than neural networks. However, compared with a random classifier (success rate = 14%), even the less successful algorithms were good at the task of classifying. The use of rapid fluorescence transients, handled by genetic programming, for rapid preliminary classification of Vitis genotypes is foreseen as feasible.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNSilva, Jorge Marques daFigueiredo, AndreiaCunha, JorgeEiras-Dias, José EduardoSilva, SaraVanneschi, LeonardoMariano, Pedro2020-02-26T23:50:01Z2020-02-012020-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article19application/pdfhttp://hdl.handle.net/10362/93403eng2223-7747PURE: 17045844https://doi.org/10.3390/plants9020174info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:41:46Zoai:run.unl.pt:10362/93403Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:37:44.331101Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Using rapid chlorophyll fluorescence transients to classify vitis genotypes |
title |
Using rapid chlorophyll fluorescence transients to classify vitis genotypes |
spellingShingle |
Using rapid chlorophyll fluorescence transients to classify vitis genotypes Silva, Jorge Marques da Artificial neural networks Chlorophyll a fluorescence Decision trees Genetic programming K-nearest neighbors Kautsky effect Molecular markers Photosynthesis Vitis Ecology, Evolution, Behavior and Systematics Ecology Plant Science SDG 15 - Life on Land |
title_short |
Using rapid chlorophyll fluorescence transients to classify vitis genotypes |
title_full |
Using rapid chlorophyll fluorescence transients to classify vitis genotypes |
title_fullStr |
Using rapid chlorophyll fluorescence transients to classify vitis genotypes |
title_full_unstemmed |
Using rapid chlorophyll fluorescence transients to classify vitis genotypes |
title_sort |
Using rapid chlorophyll fluorescence transients to classify vitis genotypes |
author |
Silva, Jorge Marques da |
author_facet |
Silva, Jorge Marques da Figueiredo, Andreia Cunha, Jorge Eiras-Dias, José Eduardo Silva, Sara Vanneschi, Leonardo Mariano, Pedro |
author_role |
author |
author2 |
Figueiredo, Andreia Cunha, Jorge Eiras-Dias, José Eduardo Silva, Sara Vanneschi, Leonardo Mariano, Pedro |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School RUN |
dc.contributor.author.fl_str_mv |
Silva, Jorge Marques da Figueiredo, Andreia Cunha, Jorge Eiras-Dias, José Eduardo Silva, Sara Vanneschi, Leonardo Mariano, Pedro |
dc.subject.por.fl_str_mv |
Artificial neural networks Chlorophyll a fluorescence Decision trees Genetic programming K-nearest neighbors Kautsky effect Molecular markers Photosynthesis Vitis Ecology, Evolution, Behavior and Systematics Ecology Plant Science SDG 15 - Life on Land |
topic |
Artificial neural networks Chlorophyll a fluorescence Decision trees Genetic programming K-nearest neighbors Kautsky effect Molecular markers Photosynthesis Vitis Ecology, Evolution, Behavior and Systematics Ecology Plant Science SDG 15 - Life on Land |
description |
Silva, J. M. da, Figueiredo, A., Cunha, J., Eiras-Dias, J. E., Silva, S., Vanneschi, L., & Mariano, P. (2020). Using rapid chlorophyll fluorescence transients to classify vitis genotypes. Plants, 9(2), 1-19. [174]. https://doi.org/10.3390/plants9020174 |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-26T23:50:01Z 2020-02-01 2020-02-01T00:00:00Z |
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://hdl.handle.net/10362/93403 |
url |
http://hdl.handle.net/10362/93403 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2223-7747 PURE: 17045844 https://doi.org/10.3390/plants9020174 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
19 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137994246979584 |