Using rapid chlorophyll fluorescence transients to classify vitis genotypes

Bibliographic Details
Main Author: Silva, Jorge Marques da
Publication Date: 2020
Other Authors: Figueiredo, Andreia, Cunha, Jorge, Eiras-Dias, José Eduardo, Silva, Sara, Vanneschi, Leonardo, Mariano, Pedro
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/93403
Summary: 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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spelling 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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-07-22T01:35:52Zoai:run.unl.pt:10362/93403Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:14:58.239005Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596551403929600

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