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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
23/05/2022 |
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Data da última atualização: |
23/05/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
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. |
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Afiliação: |
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. |
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Título: |
Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
AgriEngineering, v. 4, n. 1, p. 311-319, Mar. 2022. |
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Idioma: |
Inglês |
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Conteúdo: |
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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Palavras-Chave: |
Agricultura digital. |
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Thesagro: |
Agricultura de Precisão; Coffea Arábica; Sensoriamento Remoto. |
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Thesaurus Nal: |
Precision agriculture; Remote sensing; Unmanned aerial vehicles. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1143380/1/Vegetation-Indices-Applied-2022.pdf
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Marc: |
LEADER 02324naa a2200277 a 4500
001 2143380
005 2022-05-23
008 2022 bl uuuu u00u1 u #d
100 1 $aSANTOS, L. M. dos
245 $aVegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner.$h[electronic resource]
260 $c2022
520 $aThe 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.
650 $aPrecision agriculture
650 $aRemote sensing
650 $aUnmanned aerial vehicles
650 $aAgricultura de Precisão
650 $aCoffea Arábica
650 $aSensoriamento Remoto
653 $aAgricultura digital
700 1 $aFERRAZ, G. A. e S.
700 1 $aMARIN, D. B.
700 1 $aCARVALHO, M. A. de F.
700 1 $aDIAS, J. E. L.
700 1 $aALECRIM, A. de O.
700 1 $aSILVA, M. de L. O. e
773 $tAgriEngineering$gv. 4, n. 1, p. 311-319, Mar. 2022.
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Registro original: |
Embrapa Café (CNPCa) |
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| 1. |  | 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. Vegetation indices applied to suborbital multispectral images of healthy coffee and coffee infested with coffee leaf miner. AgriEngineering, v. 4, n. 1, p. 311-319, Mar. 2022.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 2 |
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