Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy System
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/s22020668 http://hdl.handle.net/11449/234013 |
Resumo: | Mato Grosso, Brazil, is the largest soy producer in the country. Asian Soy Rust is a disease that has already caused a lot of damage to Brazilian agribusiness. The plant matures prematurely, hindering the filling of the pod, drastically reducing productivity. It is caused by the Phakopsora pachyrhizi fungus. For a plant disease to establish itself, the presence of a pathogen, a susceptible plant, and favorable environmental conditions are necessary. This research developed a fuzzy system gathering these three variables as inputs, having as an output the vulnerability of the region to the disease. The presence of the pathogen was measured using a diffusion-advection equation appropriate to the problem. Some coefficients were based on the literature, others were measured by a fuzzy system and others were obtained by real data. From the mapping of producing properties, the locations where there are susceptible plants were established. And the favorable environmental conditions were also obtained from a fuzzy system, whose inputs were temperature and leaf wetness. Data provided by IBGE, INMET, and Antirust Consortium were used to fuel the model, and all treatments, tests, and simulations were carried out within the Matlab® environment. Although Asian Soybean Rust was the chosen disease here, the model was general in nature, so could be reproduced for any disease of plants with the same profile. |
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Repositório Institucional da UNESP |
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Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy SystemAdvection-diffusion problemFuzzy logicModeling and simulation Asian soybean rustMato Grosso, Brazil, is the largest soy producer in the country. Asian Soy Rust is a disease that has already caused a lot of damage to Brazilian agribusiness. The plant matures prematurely, hindering the filling of the pod, drastically reducing productivity. It is caused by the Phakopsora pachyrhizi fungus. For a plant disease to establish itself, the presence of a pathogen, a susceptible plant, and favorable environmental conditions are necessary. This research developed a fuzzy system gathering these three variables as inputs, having as an output the vulnerability of the region to the disease. The presence of the pathogen was measured using a diffusion-advection equation appropriate to the problem. Some coefficients were based on the literature, others were measured by a fuzzy system and others were obtained by real data. From the mapping of producing properties, the locations where there are susceptible plants were established. And the favorable environmental conditions were also obtained from a fuzzy system, whose inputs were temperature and leaf wetness. Data provided by IBGE, INMET, and Antirust Consortium were used to fuel the model, and all treatments, tests, and simulations were carried out within the Matlab® environment. Although Asian Soybean Rust was the chosen disease here, the model was general in nature, so could be reproduced for any disease of plants with the same profile.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Electrical Engineering Department UNESP-São Paulo State University, Av. Brasil 56, (SP), Sao PauloElectrical Engineering Department UNESP-São Paulo State University, Av. Brasil 56, (SP), Sao PauloCAPES: 001Universidade Estadual Paulista (UNESP)Zagui, Nayara Longo Sartor [UNESP]Krindges, André [UNESP]Lotufo, Anna Diva Plasencia [UNESP]Minussi, Carlos Roberto [UNESP]2022-05-01T12:09:45Z2022-05-01T12:09:45Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/s22020668Sensors, v. 22, n. 2, 2022.1424-8220http://hdl.handle.net/11449/23401310.3390/s220206682-s2.0-85122880401Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSensorsinfo:eu-repo/semantics/openAccess2022-05-01T12:09:45Zoai:repositorio.unesp.br:11449/234013Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-05-01T12:09:45Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy System |
title |
Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy System |
spellingShingle |
Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy System Zagui, Nayara Longo Sartor [UNESP] Advection-diffusion problem Fuzzy logic Modeling and simulation Asian soybean rust |
title_short |
Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy System |
title_full |
Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy System |
title_fullStr |
Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy System |
title_full_unstemmed |
Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy System |
title_sort |
Spatio-Temporal Modeling and Simulation of Asian Soybean Rust Based on Fuzzy System |
author |
Zagui, Nayara Longo Sartor [UNESP] |
author_facet |
Zagui, Nayara Longo Sartor [UNESP] Krindges, André [UNESP] Lotufo, Anna Diva Plasencia [UNESP] Minussi, Carlos Roberto [UNESP] |
author_role |
author |
author2 |
Krindges, André [UNESP] Lotufo, Anna Diva Plasencia [UNESP] Minussi, Carlos Roberto [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Zagui, Nayara Longo Sartor [UNESP] Krindges, André [UNESP] Lotufo, Anna Diva Plasencia [UNESP] Minussi, Carlos Roberto [UNESP] |
dc.subject.por.fl_str_mv |
Advection-diffusion problem Fuzzy logic Modeling and simulation Asian soybean rust |
topic |
Advection-diffusion problem Fuzzy logic Modeling and simulation Asian soybean rust |
description |
Mato Grosso, Brazil, is the largest soy producer in the country. Asian Soy Rust is a disease that has already caused a lot of damage to Brazilian agribusiness. The plant matures prematurely, hindering the filling of the pod, drastically reducing productivity. It is caused by the Phakopsora pachyrhizi fungus. For a plant disease to establish itself, the presence of a pathogen, a susceptible plant, and favorable environmental conditions are necessary. This research developed a fuzzy system gathering these three variables as inputs, having as an output the vulnerability of the region to the disease. The presence of the pathogen was measured using a diffusion-advection equation appropriate to the problem. Some coefficients were based on the literature, others were measured by a fuzzy system and others were obtained by real data. From the mapping of producing properties, the locations where there are susceptible plants were established. And the favorable environmental conditions were also obtained from a fuzzy system, whose inputs were temperature and leaf wetness. Data provided by IBGE, INMET, and Antirust Consortium were used to fuel the model, and all treatments, tests, and simulations were carried out within the Matlab® environment. Although Asian Soybean Rust was the chosen disease here, the model was general in nature, so could be reproduced for any disease of plants with the same profile. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-05-01T12:09:45Z 2022-05-01T12:09:45Z 2022-01-01 |
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/10.3390/s22020668 Sensors, v. 22, n. 2, 2022. 1424-8220 http://hdl.handle.net/11449/234013 10.3390/s22020668 2-s2.0-85122880401 |
url |
http://dx.doi.org/10.3390/s22020668 http://hdl.handle.net/11449/234013 |
identifier_str_mv |
Sensors, v. 22, n. 2, 2022. 1424-8220 10.3390/s22020668 2-s2.0-85122880401 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sensors |
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_ |
1799965282229288960 |