Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | https://www.sciencedirect.com/science/article/pii/S1364815211000831#! http://repositorio.ufla.br/jspui/handle/1/28446 |
Resumo: | The objective of this work was to develop and to evaluate adaptive neuro-fuzzy inference systems as methodology to describe the severity of soybean rust (Phakopsora pachyrhizi) monocyclic process in soybean [Glycine max (L.) Merr.], under effects of leaf wetness, temperature, and days after fungi inoculation. The experiment was conducted in growth chambers with mean air temperatures of 15, 20, 25 and 30 °C and leaf wetness periods of 6, 12, 18 and 24 h. The plants were inoculated by spraying a suspension of P. pachyrhizi inoculum at concentration of 104 uredinospore mL−1. A disease assessment key was adopted for estimate amounts of soybean rust at 0, 6, 9, 12 and 15 days after fungi inoculation. A hybrid neural network training with 3 and 3000 epochs was applied to disease severity data for optimization of fuzzy system parameters used to describe the severity of soybean rust based on leaf wetness, temperature and days after fungi inoculation. Higher accuracy and precision of the neuro-fuzzy systems estimates were obtained after training with 3000 epochs. Nevertheless, training with 3 epochs produced smoother estimates. The neuro-fuzzy systems enabled to describe the severity of soybean rust monocyclic process under effects of leaf wetness, mean air temperature and days after fungi inoculation and was better applied for Conquista cultivar, followed by Savana and Suprema cultivars. Higher soybean rust severity was verified under temperatures among 20 °C and 25 °C, leaf wetness above 6 h, with higher values above 10 h, and 15 days after fungi inoculation. Temperatures near 15 °C increased the latent period of the disease but not inhibited its development after 10 days of fungi inoculation. |
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Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rustSoybean – Diseases and pestsNeural networks (Computer science)Plant diseases – Computational modelsSoja – Doenças e pragasRedes neurais (Computação)Fitopatologia – Modelos computacionaisGlycine maxPhakopsora pachyrhiziThe objective of this work was to develop and to evaluate adaptive neuro-fuzzy inference systems as methodology to describe the severity of soybean rust (Phakopsora pachyrhizi) monocyclic process in soybean [Glycine max (L.) Merr.], under effects of leaf wetness, temperature, and days after fungi inoculation. The experiment was conducted in growth chambers with mean air temperatures of 15, 20, 25 and 30 °C and leaf wetness periods of 6, 12, 18 and 24 h. The plants were inoculated by spraying a suspension of P. pachyrhizi inoculum at concentration of 104 uredinospore mL−1. A disease assessment key was adopted for estimate amounts of soybean rust at 0, 6, 9, 12 and 15 days after fungi inoculation. A hybrid neural network training with 3 and 3000 epochs was applied to disease severity data for optimization of fuzzy system parameters used to describe the severity of soybean rust based on leaf wetness, temperature and days after fungi inoculation. Higher accuracy and precision of the neuro-fuzzy systems estimates were obtained after training with 3000 epochs. Nevertheless, training with 3 epochs produced smoother estimates. The neuro-fuzzy systems enabled to describe the severity of soybean rust monocyclic process under effects of leaf wetness, mean air temperature and days after fungi inoculation and was better applied for Conquista cultivar, followed by Savana and Suprema cultivars. Higher soybean rust severity was verified under temperatures among 20 °C and 25 °C, leaf wetness above 6 h, with higher values above 10 h, and 15 days after fungi inoculation. Temperatures near 15 °C increased the latent period of the disease but not inhibited its development after 10 days of fungi inoculation.Elsevier2018-01-24T13:01:54Z2018-01-24T13:01:54Z2011-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleALVES, M. de C. et al. Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust. Environmental Modelling and Software, [Oxford], v. 26, n. 9, p. 1089-1096, Sept. 2011.https://www.sciencedirect.com/science/article/pii/S1364815211000831#!http://repositorio.ufla.br/jspui/handle/1/28446Environmental Modelling and Softwarereponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAlves, Marcelo de CarvalhoPozza, Edson AmpélioCosta, João de Cássia do BonfimCarvalho, Luiz Gonsaga deAlves, Luciana Sanchesinfo:eu-repo/semantics/openAccesseng2023-05-26T19:53:30Zoai:localhost:1/28446Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-26T19:53:30Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust |
title |
Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust |
spellingShingle |
Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust Alves, Marcelo de Carvalho Soybean – Diseases and pests Neural networks (Computer science) Plant diseases – Computational models Soja – Doenças e pragas Redes neurais (Computação) Fitopatologia – Modelos computacionais Glycine max Phakopsora pachyrhizi |
title_short |
Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust |
title_full |
Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust |
title_fullStr |
Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust |
title_full_unstemmed |
Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust |
title_sort |
Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust |
author |
Alves, Marcelo de Carvalho |
author_facet |
Alves, Marcelo de Carvalho Pozza, Edson Ampélio Costa, João de Cássia do Bonfim Carvalho, Luiz Gonsaga de Alves, Luciana Sanches |
author_role |
author |
author2 |
Pozza, Edson Ampélio Costa, João de Cássia do Bonfim Carvalho, Luiz Gonsaga de Alves, Luciana Sanches |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Alves, Marcelo de Carvalho Pozza, Edson Ampélio Costa, João de Cássia do Bonfim Carvalho, Luiz Gonsaga de Alves, Luciana Sanches |
dc.subject.por.fl_str_mv |
Soybean – Diseases and pests Neural networks (Computer science) Plant diseases – Computational models Soja – Doenças e pragas Redes neurais (Computação) Fitopatologia – Modelos computacionais Glycine max Phakopsora pachyrhizi |
topic |
Soybean – Diseases and pests Neural networks (Computer science) Plant diseases – Computational models Soja – Doenças e pragas Redes neurais (Computação) Fitopatologia – Modelos computacionais Glycine max Phakopsora pachyrhizi |
description |
The objective of this work was to develop and to evaluate adaptive neuro-fuzzy inference systems as methodology to describe the severity of soybean rust (Phakopsora pachyrhizi) monocyclic process in soybean [Glycine max (L.) Merr.], under effects of leaf wetness, temperature, and days after fungi inoculation. The experiment was conducted in growth chambers with mean air temperatures of 15, 20, 25 and 30 °C and leaf wetness periods of 6, 12, 18 and 24 h. The plants were inoculated by spraying a suspension of P. pachyrhizi inoculum at concentration of 104 uredinospore mL−1. A disease assessment key was adopted for estimate amounts of soybean rust at 0, 6, 9, 12 and 15 days after fungi inoculation. A hybrid neural network training with 3 and 3000 epochs was applied to disease severity data for optimization of fuzzy system parameters used to describe the severity of soybean rust based on leaf wetness, temperature and days after fungi inoculation. Higher accuracy and precision of the neuro-fuzzy systems estimates were obtained after training with 3000 epochs. Nevertheless, training with 3 epochs produced smoother estimates. The neuro-fuzzy systems enabled to describe the severity of soybean rust monocyclic process under effects of leaf wetness, mean air temperature and days after fungi inoculation and was better applied for Conquista cultivar, followed by Savana and Suprema cultivars. Higher soybean rust severity was verified under temperatures among 20 °C and 25 °C, leaf wetness above 6 h, with higher values above 10 h, and 15 days after fungi inoculation. Temperatures near 15 °C increased the latent period of the disease but not inhibited its development after 10 days of fungi inoculation. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-09 2018-01-24T13:01:54Z 2018-01-24T13:01:54Z |
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 |
ALVES, M. de C. et al. Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust. Environmental Modelling and Software, [Oxford], v. 26, n. 9, p. 1089-1096, Sept. 2011. https://www.sciencedirect.com/science/article/pii/S1364815211000831#! http://repositorio.ufla.br/jspui/handle/1/28446 |
identifier_str_mv |
ALVES, M. de C. et al. Adaptive neuro-fuzzy inference systems for epidemiological analysis of soybean rust. Environmental Modelling and Software, [Oxford], v. 26, n. 9, p. 1089-1096, Sept. 2011. |
url |
https://www.sciencedirect.com/science/article/pii/S1364815211000831#! http://repositorio.ufla.br/jspui/handle/1/28446 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
Environmental Modelling and Software reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835163063746560 |