New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery.
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
Texto Completo: | http://www.alice.cnptia.embrapa.br/alice/handle/doc/1162117 https://doi.org/10.25186/.v18i.2170 |
Resumo: | Coffee Rust (Hemileia vastatrix) is considered the primary coffee disease in the world. The pathogenic fungus can find favorable environmental conditions in different countries, constantly threatening coffee producers. The previous detection of the incidence of coffee rust in a region is crucial because it provides an overview of the disease’s progress aiding in coffee plantations management. The objective of this work was the development of a vegetation index for remote monitoring of coffee rust infestation. Using satellite images from the MSI/Sentinel-2 collection, the Machine Learning classifier algorithm - Random Forest, and the cloud processing platform - Google Earth Engine, the most sensitives bands in coffee rust detection were determined, namely B4 (Red), B7 (Red Edge 3) and B8A (Red Edge 4). Thus, the Triangular Vegetation Index method was used to create a new vegetative index for remote detection of coffee rust infestation on a regional scale, named Coffee Rust Detection Index (CRDI). A linear regression model was created to estimate rust infestation based on the performance of the new index. The model presented a coefficient of determination (R²) of 62.5%, and a root mean square error (RMSE) of 0.107. In addition, a comparison analysis of the new index with eight other vegetative indices commonly used in the literature was carried out. The CRDI obtained the best performance in coffee rust detection among the others. This study shows that the new index CRDI has the robustness and general capacity to be used in monitoring coffee rust infestation on a regional scale. |
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New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery.Hemileia VastatrixDisease controlVegetation indexControl methodsCoffee Rust (Hemileia vastatrix) is considered the primary coffee disease in the world. The pathogenic fungus can find favorable environmental conditions in different countries, constantly threatening coffee producers. The previous detection of the incidence of coffee rust in a region is crucial because it provides an overview of the disease’s progress aiding in coffee plantations management. The objective of this work was the development of a vegetation index for remote monitoring of coffee rust infestation. Using satellite images from the MSI/Sentinel-2 collection, the Machine Learning classifier algorithm - Random Forest, and the cloud processing platform - Google Earth Engine, the most sensitives bands in coffee rust detection were determined, namely B4 (Red), B7 (Red Edge 3) and B8A (Red Edge 4). Thus, the Triangular Vegetation Index method was used to create a new vegetative index for remote detection of coffee rust infestation on a regional scale, named Coffee Rust Detection Index (CRDI). A linear regression model was created to estimate rust infestation based on the performance of the new index. The model presented a coefficient of determination (R²) of 62.5%, and a root mean square error (RMSE) of 0.107. In addition, a comparison analysis of the new index with eight other vegetative indices commonly used in the literature was carried out. The CRDI obtained the best performance in coffee rust detection among the others. This study shows that the new index CRDI has the robustness and general capacity to be used in monitoring coffee rust infestation on a regional scale.GABRIEL DUMBÁ MONTEIRO DE CASTRO, UNIVERSIDADE FEDERAL DE VIÇOSA; EMERSON FERREIRA VILELA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; ANA LUÍSA RIBEIRO DE FARIA, UNIVERSIDADE FEDERAL DE VIÇOSA; ROGÉRIO ANTÔNIO SILVA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; WILLIAMS PINTO MARQUES FERREIRA, CNPCa.CASTRO, G. D. M. deVILELA, E. F.FARIA, A. L. R. deSILVA, R. A.FERREIRA, W. P. M.2024-02-19T20:32:42Z2024-02-19T20:32:42Z2024-02-192023info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleCoffee Science, v. 18, e182170, 2023.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1162117https://doi.org/10.25186/.v18i.2170porinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2024-02-19T20:32:42Zoai:www.alice.cnptia.embrapa.br:doc/1162117Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542024-02-19T20:32:42falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542024-02-19T20:32:42Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false |
dc.title.none.fl_str_mv |
New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery. |
title |
New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery. |
spellingShingle |
New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery. CASTRO, G. D. M. de Hemileia Vastatrix Disease control Vegetation index Control methods |
title_short |
New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery. |
title_full |
New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery. |
title_fullStr |
New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery. |
title_full_unstemmed |
New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery. |
title_sort |
New vegetation index for monitoring coffee rust using sentinel-2 multispectral imagery. |
author |
CASTRO, G. D. M. de |
author_facet |
CASTRO, G. D. M. de VILELA, E. F. FARIA, A. L. R. de SILVA, R. A. FERREIRA, W. P. M. |
author_role |
author |
author2 |
VILELA, E. F. FARIA, A. L. R. de SILVA, R. A. FERREIRA, W. P. M. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
GABRIEL DUMBÁ MONTEIRO DE CASTRO, UNIVERSIDADE FEDERAL DE VIÇOSA; EMERSON FERREIRA VILELA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; ANA LUÍSA RIBEIRO DE FARIA, UNIVERSIDADE FEDERAL DE VIÇOSA; ROGÉRIO ANTÔNIO SILVA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; WILLIAMS PINTO MARQUES FERREIRA, CNPCa. |
dc.contributor.author.fl_str_mv |
CASTRO, G. D. M. de VILELA, E. F. FARIA, A. L. R. de SILVA, R. A. FERREIRA, W. P. M. |
dc.subject.por.fl_str_mv |
Hemileia Vastatrix Disease control Vegetation index Control methods |
topic |
Hemileia Vastatrix Disease control Vegetation index Control methods |
description |
Coffee Rust (Hemileia vastatrix) is considered the primary coffee disease in the world. The pathogenic fungus can find favorable environmental conditions in different countries, constantly threatening coffee producers. The previous detection of the incidence of coffee rust in a region is crucial because it provides an overview of the disease’s progress aiding in coffee plantations management. The objective of this work was the development of a vegetation index for remote monitoring of coffee rust infestation. Using satellite images from the MSI/Sentinel-2 collection, the Machine Learning classifier algorithm - Random Forest, and the cloud processing platform - Google Earth Engine, the most sensitives bands in coffee rust detection were determined, namely B4 (Red), B7 (Red Edge 3) and B8A (Red Edge 4). Thus, the Triangular Vegetation Index method was used to create a new vegetative index for remote detection of coffee rust infestation on a regional scale, named Coffee Rust Detection Index (CRDI). A linear regression model was created to estimate rust infestation based on the performance of the new index. The model presented a coefficient of determination (R²) of 62.5%, and a root mean square error (RMSE) of 0.107. In addition, a comparison analysis of the new index with eight other vegetative indices commonly used in the literature was carried out. The CRDI obtained the best performance in coffee rust detection among the others. This study shows that the new index CRDI has the robustness and general capacity to be used in monitoring coffee rust infestation on a regional scale. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2024-02-19T20:32:42Z 2024-02-19T20:32:42Z 2024-02-19 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Coffee Science, v. 18, e182170, 2023. http://www.alice.cnptia.embrapa.br/alice/handle/doc/1162117 https://doi.org/10.25186/.v18i.2170 |
identifier_str_mv |
Coffee Science, v. 18, e182170, 2023. |
url |
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1162117 https://doi.org/10.25186/.v18i.2170 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA |
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Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
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EMBRAPA |
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EMBRAPA |
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Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
collection |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) |
repository.name.fl_str_mv |
Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa) |
repository.mail.fl_str_mv |
cg-riaa@embrapa.br |
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1794503558577520640 |