Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.

Detalhes bibliográficos
Autor(a) principal: SANTOS, L. M. dos
Data de Publicação: 2022
Outros Autores: FERRAZ, G. A. e S., MARIN, D. B., CARVALHO, M. A. de F., DIAS, J. E. L., ALECRIM, A. de O., SILVA, M. de L. O. e
Tipo de documento: Artigo
Idioma: eng
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/1143380
Resumo: The coffee leaf miner (Leucoptera coffeella) is a primary pest for coffee plants. The attack of this pest reduces the photosynthetic area of the leaves due to necrosis, causing premature leaf falling, decreasing the yield and the lifespan of the plant. Therefore, this study aims to analyze vegetation indices (VI) from images of healthy coffee leaves and those infested by coffee leaf miner, obtained using a multispectral camera, mainly to differentiate and detect infested areas. The study was conducted in two distinct locations: At a farm, where the camera was coupled to a remotely piloted aircraft (RPA) flying at a 3 m altitude from the soil surface; and the second location, in a greenhouse, where the images were obtained manually at a 0.5 m altitude from the support of the plant vessels, in which only healthy plants were located. For the image processing, arithmetic operations with the spectral bands were calculated using the ?Raster Calculator? obtaining the indices NormNIR, Normalized Difference Vegetation Index (NDVI), Green-Red NDVI (GRNDVI), and Green NDVI (GNDVI), the values of which on average for healthy leaves were: 0.66; 0.64; 0.32, and 0.55 and for infested leaves: 0.53; 0.41; 0.06, and 0.37 respectively. The analysis concluded that healthy leaves presented higher values of VIs when compared to infested leaves. The index GRNDVI was the one that better differentiated infested leaves from the healthy ones.
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spelling Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.Agricultura digitalAgricultura de PrecisãoSensoriamento RemotoCoffea ArábicaPrecision agricultureRemote sensingUnmanned aerial vehiclesThe coffee leaf miner (Leucoptera coffeella) is a primary pest for coffee plants. The attack of this pest reduces the photosynthetic area of the leaves due to necrosis, causing premature leaf falling, decreasing the yield and the lifespan of the plant. Therefore, this study aims to analyze vegetation indices (VI) from images of healthy coffee leaves and those infested by coffee leaf miner, obtained using a multispectral camera, mainly to differentiate and detect infested areas. The study was conducted in two distinct locations: At a farm, where the camera was coupled to a remotely piloted aircraft (RPA) flying at a 3 m altitude from the soil surface; and the second location, in a greenhouse, where the images were obtained manually at a 0.5 m altitude from the support of the plant vessels, in which only healthy plants were located. For the image processing, arithmetic operations with the spectral bands were calculated using the ?Raster Calculator? obtaining the indices NormNIR, Normalized Difference Vegetation Index (NDVI), Green-Red NDVI (GRNDVI), and Green NDVI (GNDVI), the values of which on average for healthy leaves were: 0.66; 0.64; 0.32, and 0.55 and for infested leaves: 0.53; 0.41; 0.06, and 0.37 respectively. The analysis concluded that healthy leaves presented higher values of VIs when compared to infested leaves. The index GRNDVI was the one that better differentiated infested leaves from the healthy ones.LUANA MENDES DOS SANTOS, UFLA; GABRIEL ARAÚJO E SILVA FERRAZ, UFLA; DIEGO BEDIN MARIN, UFLA; MILENE ALVES DE FIGUEIREDO CARVALHO, CNPCa; JESSICA ELLEN LIMA DIAS, HUNGARIAN UNIVERSITY OF AGRICULTURE AND LIFE SCIENCES; ADEMILSON DE OLIVEIRA ALECRIM, UFLA; MIRIAN DE LOURDES OLIVEIRA E SILVA, UFLA.SANTOS, L. M. dosFERRAZ, G. A. e S.MARIN, D. B.CARVALHO, M. A. de F.DIAS, J. E. L.ALECRIM, A. de O.SILVA, M. de L. O. e2022-05-24T05:04:25Z2022-05-24T05:04:25Z2022-05-232022info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleAgriEngineering, v. 4, n. 1, p. 311-319, Mar. 2022.http://www.alice.cnptia.embrapa.br/alice/handle/doc/1143380enginfo: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:EMBRAPA2022-05-24T05:04:34Zoai:www.alice.cnptia.embrapa.br:doc/1143380Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542022-05-24T05:04:34falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542022-05-24T05:04:34Repositório 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 Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.
title Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.
spellingShingle Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.
SANTOS, L. M. dos
Agricultura digital
Agricultura de Precisão
Sensoriamento Remoto
Coffea Arábica
Precision agriculture
Remote sensing
Unmanned aerial vehicles
title_short Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.
title_full Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.
title_fullStr Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.
title_full_unstemmed Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.
title_sort Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.
author SANTOS, L. M. dos
author_facet SANTOS, L. M. dos
FERRAZ, G. A. e S.
MARIN, D. B.
CARVALHO, M. A. de F.
DIAS, J. E. L.
ALECRIM, A. de O.
SILVA, M. de L. O. e
author_role author
author2 FERRAZ, G. A. e S.
MARIN, D. B.
CARVALHO, M. A. de F.
DIAS, J. E. L.
ALECRIM, A. de O.
SILVA, M. de L. O. e
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv LUANA MENDES DOS SANTOS, UFLA; GABRIEL ARAÚJO E SILVA FERRAZ, UFLA; DIEGO BEDIN MARIN, UFLA; MILENE ALVES DE FIGUEIREDO CARVALHO, CNPCa; JESSICA ELLEN LIMA DIAS, HUNGARIAN UNIVERSITY OF AGRICULTURE AND LIFE SCIENCES; ADEMILSON DE OLIVEIRA ALECRIM, UFLA; MIRIAN DE LOURDES OLIVEIRA E SILVA, UFLA.
dc.contributor.author.fl_str_mv SANTOS, L. M. dos
FERRAZ, G. A. e S.
MARIN, D. B.
CARVALHO, M. A. de F.
DIAS, J. E. L.
ALECRIM, A. de O.
SILVA, M. de L. O. e
dc.subject.por.fl_str_mv Agricultura digital
Agricultura de Precisão
Sensoriamento Remoto
Coffea Arábica
Precision agriculture
Remote sensing
Unmanned aerial vehicles
topic Agricultura digital
Agricultura de Precisão
Sensoriamento Remoto
Coffea Arábica
Precision agriculture
Remote sensing
Unmanned aerial vehicles
description The coffee leaf miner (Leucoptera coffeella) is a primary pest for coffee plants. The attack of this pest reduces the photosynthetic area of the leaves due to necrosis, causing premature leaf falling, decreasing the yield and the lifespan of the plant. Therefore, this study aims to analyze vegetation indices (VI) from images of healthy coffee leaves and those infested by coffee leaf miner, obtained using a multispectral camera, mainly to differentiate and detect infested areas. The study was conducted in two distinct locations: At a farm, where the camera was coupled to a remotely piloted aircraft (RPA) flying at a 3 m altitude from the soil surface; and the second location, in a greenhouse, where the images were obtained manually at a 0.5 m altitude from the support of the plant vessels, in which only healthy plants were located. For the image processing, arithmetic operations with the spectral bands were calculated using the ?Raster Calculator? obtaining the indices NormNIR, Normalized Difference Vegetation Index (NDVI), Green-Red NDVI (GRNDVI), and Green NDVI (GNDVI), the values of which on average for healthy leaves were: 0.66; 0.64; 0.32, and 0.55 and for infested leaves: 0.53; 0.41; 0.06, and 0.37 respectively. The analysis concluded that healthy leaves presented higher values of VIs when compared to infested leaves. The index GRNDVI was the one that better differentiated infested leaves from the healthy ones.
publishDate 2022
dc.date.none.fl_str_mv 2022-05-24T05:04:25Z
2022-05-24T05:04:25Z
2022-05-23
2022
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 AgriEngineering, v. 4, n. 1, p. 311-319, Mar. 2022.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/1143380
identifier_str_mv AgriEngineering, v. 4, n. 1, p. 311-319, Mar. 2022.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/1143380
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.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
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str 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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