Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/JSTARS.2017.2737618 http://hdl.handle.net/11449/163672 |
Resumo: | Nematodes are a serious issue for coffee cultivation in Brazil. Because root infection by nematodes induces spectral variation in leaves and defines a unique spatial configuration in the cultivation field, the aim of this study was to use biophysical parameters and remote sensing data to discriminate and map healthy, moderately infected, and severely infected coffee plants. An experimental area in southern Minas Gerais State, in which the occurrence of nematodes was certified, was selected, and biophysical and spectral measurements of the leaves were made. Hyperspectral data were also used in a band simulation of the RapidEye sensor to identify the most sensitive spectral ranges for pathogen discrimination in coffee plants. These bands, plus a normalized difference vegetation index image, were used for a multispectral classification of healthy and nematode-infected areas. None of the biophysical parameters efficiently discriminated the leaves of healthy and infected plants, but the band simulation indicated that red, red edge, and near infrared spectral ranges were complementary to the discrimination of healthy coffee plants and the two levels of infection. The multispectral classification defined the spatial distribution of healthy, moderately infected, and severely infected coffee plants, with an overall accuracy of 78% and Kappa coefficient of 0.71. Consideringthe degree of uncertainty and high cost involved in conventional detection of soil parasites, thelevels of accuracy achieved were adequate. |
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Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing MeasurementsCoffee plantationdisease detectionmappingnematodesprecision agricultureremote sensingspectral characterizationNematodes are a serious issue for coffee cultivation in Brazil. Because root infection by nematodes induces spectral variation in leaves and defines a unique spatial configuration in the cultivation field, the aim of this study was to use biophysical parameters and remote sensing data to discriminate and map healthy, moderately infected, and severely infected coffee plants. An experimental area in southern Minas Gerais State, in which the occurrence of nematodes was certified, was selected, and biophysical and spectral measurements of the leaves were made. Hyperspectral data were also used in a band simulation of the RapidEye sensor to identify the most sensitive spectral ranges for pathogen discrimination in coffee plants. These bands, plus a normalized difference vegetation index image, were used for a multispectral classification of healthy and nematode-infected areas. None of the biophysical parameters efficiently discriminated the leaves of healthy and infected plants, but the band simulation indicated that red, red edge, and near infrared spectral ranges were complementary to the discrimination of healthy coffee plants and the two levels of infection. The multispectral classification defined the spatial distribution of healthy, moderately infected, and severely infected coffee plants, with an overall accuracy of 78% and Kappa coefficient of 0.71. Consideringthe degree of uncertainty and high cost involved in conventional detection of soil parasites, thelevels of accuracy achieved were adequate.Sao Paulo State Univ, Grad Program Cartog Sci, BR-19060900 Presidente Prudente, SP, BrazilSao Paulo State Univ, Dept Cartog, BR-19060900 Presidente Prudente, SP, BrazilUniv Fed Uberlandia, Inst Agr Sci, BR-38500000 Mt Carmelo, MG, BrazilSao Paulo State Univ, Grad Program Cartog Sci, BR-19060900 Presidente Prudente, SP, BrazilSao Paulo State Univ, Dept Cartog, BR-19060900 Presidente Prudente, SP, BrazilIeee-inst Electrical Electronics Engineers IncUniversidade Estadual Paulista (Unesp)Universidade Federal de Uberlândia (UFU)Martins, George Deroco [UNESP]Bueno Trindade Galo, Maria de Lourdes [UNESP]Vieira, Bruno Sergio2018-11-26T17:44:31Z2018-11-26T17:44:31Z2017-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article5395-5403application/pdfhttp://dx.doi.org/10.1109/JSTARS.2017.2737618Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 10, n. 12, p. 5395-5403, 2017.1939-1404http://hdl.handle.net/11449/16367210.1109/JSTARS.2017.2737618WOS:000418871200007WOS000418871200007.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing1,547info:eu-repo/semantics/openAccess2024-06-18T15:01:26Zoai:repositorio.unesp.br:11449/163672Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:04:11.569088Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements |
title |
Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements |
spellingShingle |
Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements Martins, George Deroco [UNESP] Coffee plantation disease detection mapping nematodes precision agriculture remote sensing spectral characterization |
title_short |
Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements |
title_full |
Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements |
title_fullStr |
Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements |
title_full_unstemmed |
Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements |
title_sort |
Detecting and Mapping Root-Knot Nematode Infection in Coffee Crop Using Remote Sensing Measurements |
author |
Martins, George Deroco [UNESP] |
author_facet |
Martins, George Deroco [UNESP] Bueno Trindade Galo, Maria de Lourdes [UNESP] Vieira, Bruno Sergio |
author_role |
author |
author2 |
Bueno Trindade Galo, Maria de Lourdes [UNESP] Vieira, Bruno Sergio |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de Uberlândia (UFU) |
dc.contributor.author.fl_str_mv |
Martins, George Deroco [UNESP] Bueno Trindade Galo, Maria de Lourdes [UNESP] Vieira, Bruno Sergio |
dc.subject.por.fl_str_mv |
Coffee plantation disease detection mapping nematodes precision agriculture remote sensing spectral characterization |
topic |
Coffee plantation disease detection mapping nematodes precision agriculture remote sensing spectral characterization |
description |
Nematodes are a serious issue for coffee cultivation in Brazil. Because root infection by nematodes induces spectral variation in leaves and defines a unique spatial configuration in the cultivation field, the aim of this study was to use biophysical parameters and remote sensing data to discriminate and map healthy, moderately infected, and severely infected coffee plants. An experimental area in southern Minas Gerais State, in which the occurrence of nematodes was certified, was selected, and biophysical and spectral measurements of the leaves were made. Hyperspectral data were also used in a band simulation of the RapidEye sensor to identify the most sensitive spectral ranges for pathogen discrimination in coffee plants. These bands, plus a normalized difference vegetation index image, were used for a multispectral classification of healthy and nematode-infected areas. None of the biophysical parameters efficiently discriminated the leaves of healthy and infected plants, but the band simulation indicated that red, red edge, and near infrared spectral ranges were complementary to the discrimination of healthy coffee plants and the two levels of infection. The multispectral classification defined the spatial distribution of healthy, moderately infected, and severely infected coffee plants, with an overall accuracy of 78% and Kappa coefficient of 0.71. Consideringthe degree of uncertainty and high cost involved in conventional detection of soil parasites, thelevels of accuracy achieved were adequate. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-12-01 2018-11-26T17:44:31Z 2018-11-26T17:44:31Z |
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/10.1109/JSTARS.2017.2737618 Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 10, n. 12, p. 5395-5403, 2017. 1939-1404 http://hdl.handle.net/11449/163672 10.1109/JSTARS.2017.2737618 WOS:000418871200007 WOS000418871200007.pdf |
url |
http://dx.doi.org/10.1109/JSTARS.2017.2737618 http://hdl.handle.net/11449/163672 |
identifier_str_mv |
Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 10, n. 12, p. 5395-5403, 2017. 1939-1404 10.1109/JSTARS.2017.2737618 WOS:000418871200007 WOS000418871200007.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing 1,547 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
5395-5403 application/pdf |
dc.publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
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 |
|
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1808128749625933824 |