Weather-based prediction of anthracnose severity using artificial neural network models
Autor(a) principal: | |
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Data de Publicação: | 2004 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1111/j.1365-3059.2004.01044.x http://hdl.handle.net/11449/35002 |
Resumo: | Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development. |
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Weather-based prediction of anthracnose severity using artificial neural network modelsanthracnoseColletotrichum gloeosporioidesdisease risk and severitymultiple linear regression analysisStylosanthes scabraData were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.CSIRO Plant Ind, Queensland Biosci Precinct, St Lucia, Qld 4067, AustraliaUniv Ballarat, Sch Informat Technol & Math Sci, Ballarat, Vic 3353, AustraliaUniv Estadual Paulista Julio Mesquita Filho, FCA, EMBRAPA, CNPGC, BR-18609490 Botucatu, SP, BrazilEMBRAPA, CPAC, BR-73301970 Planaltina, DF, BrazilCtr Int Agr Trop, Cali, ColombiaUniv Estadual Paulista Julio Mesquita Filho, FCA, CNPGC, BR-18609490 Botucatu, SP, BrazilBlackwell PublishingCSIRO Plant IndUniversity of BallaratUniversidade Estadual Paulista (Unesp)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Centro Internacional de Agricultura TropicalChakraborty, S.Ghosh, R.Ghosh, M.Fernandes, C. D. [UNESP]Charchar, M. J.Kelemu, S.2014-05-20T15:24:23Z2014-05-20T15:24:23Z2004-08-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article375-386application/pdfhttp://dx.doi.org/10.1111/j.1365-3059.2004.01044.xPlant Pathology. Oxford: Blackwell Publishing Ltd, v. 53, n. 4, p. 375-386, 2004.0032-0862http://hdl.handle.net/11449/3500210.1111/j.1365-3059.2004.01044.xWOS:000223495200001WOS000223495200001.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPlant Pathology2.3031,063info:eu-repo/semantics/openAccess2023-10-18T06:04:33Zoai:repositorio.unesp.br:11449/35002Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-10-18T06:04:33Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Weather-based prediction of anthracnose severity using artificial neural network models |
title |
Weather-based prediction of anthracnose severity using artificial neural network models |
spellingShingle |
Weather-based prediction of anthracnose severity using artificial neural network models Chakraborty, S. anthracnose Colletotrichum gloeosporioides disease risk and severity multiple linear regression analysis Stylosanthes scabra |
title_short |
Weather-based prediction of anthracnose severity using artificial neural network models |
title_full |
Weather-based prediction of anthracnose severity using artificial neural network models |
title_fullStr |
Weather-based prediction of anthracnose severity using artificial neural network models |
title_full_unstemmed |
Weather-based prediction of anthracnose severity using artificial neural network models |
title_sort |
Weather-based prediction of anthracnose severity using artificial neural network models |
author |
Chakraborty, S. |
author_facet |
Chakraborty, S. Ghosh, R. Ghosh, M. Fernandes, C. D. [UNESP] Charchar, M. J. Kelemu, S. |
author_role |
author |
author2 |
Ghosh, R. Ghosh, M. Fernandes, C. D. [UNESP] Charchar, M. J. Kelemu, S. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
CSIRO Plant Ind University of Ballarat Universidade Estadual Paulista (Unesp) Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Centro Internacional de Agricultura Tropical |
dc.contributor.author.fl_str_mv |
Chakraborty, S. Ghosh, R. Ghosh, M. Fernandes, C. D. [UNESP] Charchar, M. J. Kelemu, S. |
dc.subject.por.fl_str_mv |
anthracnose Colletotrichum gloeosporioides disease risk and severity multiple linear regression analysis Stylosanthes scabra |
topic |
anthracnose Colletotrichum gloeosporioides disease risk and severity multiple linear regression analysis Stylosanthes scabra |
description |
Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development. |
publishDate |
2004 |
dc.date.none.fl_str_mv |
2004-08-01 2014-05-20T15:24:23Z 2014-05-20T15:24:23Z |
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/10.1111/j.1365-3059.2004.01044.x Plant Pathology. Oxford: Blackwell Publishing Ltd, v. 53, n. 4, p. 375-386, 2004. 0032-0862 http://hdl.handle.net/11449/35002 10.1111/j.1365-3059.2004.01044.x WOS:000223495200001 WOS000223495200001.pdf |
url |
http://dx.doi.org/10.1111/j.1365-3059.2004.01044.x http://hdl.handle.net/11449/35002 |
identifier_str_mv |
Plant Pathology. Oxford: Blackwell Publishing Ltd, v. 53, n. 4, p. 375-386, 2004. 0032-0862 10.1111/j.1365-3059.2004.01044.x WOS:000223495200001 WOS000223495200001.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Plant Pathology 2.303 1,063 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
375-386 application/pdf |
dc.publisher.none.fl_str_mv |
Blackwell Publishing |
publisher.none.fl_str_mv |
Blackwell Publishing |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1799964603419983872 |