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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agroenergia. Para informações adicionais entre em contato com cnpae.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agroenergia. |
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Data corrente: |
22/01/2015 |
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Data da última atualização: |
22/01/2015 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
MARTINS, P. K.; DIAS, B. B. A.; RIBEIRO, A. P.; SOUTO, B. de M.; KOBAYASHI, A. K.; MOLINARI, H. B. C. |
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Afiliação: |
POLYANA K. MARTINS; BARBARA ANDRADE DIAS BRITO DA CUNHA, CNPAE; ANA PAULA RIBEIRO; BETULIA DE MORAIS SOUTO, CNPAE; ADILSON KENJI KOBAYASHI, CNPAE; HUGO BRUNO CORREA MOLINARI, CNPAE. |
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Título: |
Isolation, in silico analysis and transformation of Setaria viridis with a sugarcane senescence-associated promoter. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
In: INTERNATIONAL SERTARIA GENETICS CONFERENCE, 1., 2014, Beijing, China. [Beijing]: China Agricultural Research Systems, 2014. |
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Páginas: |
p. 25. |
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Idioma: |
Inglês |
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Palavras-Chave: |
Bioetanol; Cana-de-açúcar; Matéria-prima. |
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Thesagro: |
Agrobacterium Tumefaciens; Bioenergia; Biomassa; Biotecnologia; Sacarose. |
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Thesaurus Nal: |
Setaria viridis. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00908nam a2200277 a 4500
001 2006432
005 2015-01-22
008 2014 bl uuuu u00u1 u #d
100 1 $aMARTINS, P. K.
245 $aIsolation, in silico analysis and transformation of Setaria viridis with a sugarcane senescence-associated promoter.$h[electronic resource]
260 $aIn: INTERNATIONAL SERTARIA GENETICS CONFERENCE, 1., 2014, Beijing, China. [Beijing]: China Agricultural Research Systems$c2014
300 $ap. 25.
650 $aSetaria viridis
650 $aAgrobacterium Tumefaciens
650 $aBioenergia
650 $aBiomassa
650 $aBiotecnologia
650 $aSacarose
653 $aBioetanol
653 $aCana-de-açúcar
653 $aMatéria-prima
700 1 $aDIAS, B. B. A.
700 1 $aRIBEIRO, A. P.
700 1 $aSOUTO, B. de M.
700 1 $aKOBAYASHI, A. K.
700 1 $aMOLINARI, H. B. C.
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Registro original: |
Embrapa Agroenergia (CNPAE) |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
28/02/2023 |
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Data da última atualização: |
28/02/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 3 |
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Autoria: |
VILELA, E. F.; FERREIRA, W. P. M.; CASTRO, G. D. M. de; FARIA, A. L. R. de; LEITE, D. H.; LIMA, I. A.; MATOS, C. de S. M. de; SILVA, R. A.; VENZON, M. |
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Afiliação: |
EMERSON FERREIRA VILELA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; WILLIAMS PINTO MARQUES FERREIRA, CNPCa; GABRIEL DUMBÁ MONTEIRO DE CASTRO, UNIVERSIDADE FEDERAL DE VIÇOSA; ANA LUÍSA RIBEIRO DE FARIA, UNIVERSIDADE FEDERAL DE VIÇOSA; DANIEL HENRIQUE LEITE, UNIVERSIDADE FEDERAL DE VIÇOSA; IGOR ARANTES LIMA, UNIVERSIDADE FEDERAL DE LAVRAS; CHRISTIANO DE SOUSA MACHADO DE MATOS, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; ROGÉRIO ANTONIO SILVA, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS; MADELAINE VENZON, EMPRESA DE PESQUISA AGROPECUÁRIA DE MINAS GERAIS. |
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Título: |
New spectral index and machine learning models for detecting coffee leaf miner infestation using sentinel-2 multispectral imagery. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Agriculture, v.13, n. 2, 388, 2023. |
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Páginas: |
17 p. |
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DOI: |
https://doi.org/10.3390/agriculture13020388 |
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Idioma: |
Inglês |
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Conteúdo: |
The coffee leaf miner (Leucoptera coffeella) is a key coffee pest in Brazil that can cause severe defoliation and a negative impact on the productivity. Thus, it is essential to identify initial pest infestation for the sake of appropriate time control to avoid further economic damage to the coffee crops. A fast non-destructive method is an important tool that can be used to monitor the occurrence of the coffee leaf miner. The present work aims to identify the occurrence of coffee leaf miner infestation through a new vegetation index, using multispectral images from the Sentinel-2 satellite and the Google Earth Engine platform. Coffee leaf miner infestation was measured in the field in four cities in the state of Minas Gerais. The largest infestations occurred in September, October, and November but particularly in October 2021, in which the rate of infestation reached 85%, followed by September 2020 with a maximum infestation of 76%. The calculation steps of the vegetation indices and mappings were carried out in the Google Earth Engine cloud processing platform through the development of a script in JavaScript programming language. Combinations of two sensitive bands were selected to detect coffee leaf miner infestation, and from these, the 'Coffee-Leaf-Miner Index' was developed, which was compared with other existing vegetation indices in terms of their performance for coffee leaf miner detection. The combination of the NIR-BLUE and NIR-RED bands was more sensitive for the detection of coffee leaf miner infestation; therefore, the NIR, BLUE, and RED bands were selected to develop the new index. The 'Coffee-Leaf-Miner Index' presented the best performance among those evaluated, with a coefficient of determination of about 0.87, a root mean square error of 4.92% coffee leaf miner infestation, accuracy of 89.47%, and kappa coefficient of 95.39. The R2 range of other spectral indices which exist in the literature and which were used in this study was from 0.017 to 0.867, and the root mean square error ranged from 4.996 to 13.582% coffee leaf miner infestation. The machine learning method was then adopted using the supervised Random Forest and Support Vector Machine algorithms to recognize patterns of coffee leaf miner infestation in the field, only the Coffee-Leaf-Miner Index was used for the identification test of the coffee leaf miner infestation. The Support Vector Machine with linear Kernel type was applied to establish a discrimination model. The number of trees for the Random Forest classifier was 100. The Support Vector Machine presented a lower performance than the Random Forest algorithm, but the performance of both were above 80% for user and producer precision. Three bands (Blue, Red, NIR) were selected for the creation of the new index, which showed capacity for remote detection of coffee leaf miner infestation on a regional scale. Thus, 'Coffee-Leaf-Miner Index' can identify coffee leaf miner infestation thanks to all the complexity involved in detecting pests via orbital remote sensing. MenosThe coffee leaf miner (Leucoptera coffeella) is a key coffee pest in Brazil that can cause severe defoliation and a negative impact on the productivity. Thus, it is essential to identify initial pest infestation for the sake of appropriate time control to avoid further economic damage to the coffee crops. A fast non-destructive method is an important tool that can be used to monitor the occurrence of the coffee leaf miner. The present work aims to identify the occurrence of coffee leaf miner infestation through a new vegetation index, using multispectral images from the Sentinel-2 satellite and the Google Earth Engine platform. Coffee leaf miner infestation was measured in the field in four cities in the state of Minas Gerais. The largest infestations occurred in September, October, and November but particularly in October 2021, in which the rate of infestation reached 85%, followed by September 2020 with a maximum infestation of 76%. The calculation steps of the vegetation indices and mappings were carried out in the Google Earth Engine cloud processing platform through the development of a script in JavaScript programming language. Combinations of two sensitive bands were selected to detect coffee leaf miner infestation, and from these, the 'Coffee-Leaf-Miner Index' was developed, which was compared with other existing vegetation indices in terms of their performance for coffee leaf miner detection. The combination of the NIR-BLUE and NIR-RED bands was more sensitive for t... Mostrar Tudo |
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Palavras-Chave: |
Leaf miner infestation. |
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Thesagro: |
Bicho Mineiro; Infestação; Leucoptera Coffeella. |
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Thesaurus NAL: |
Application methods; Plant pests; Remote sensing; Spectral analysis. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1151979/1/New-Spectral-Index-and-Machine-Learning-Models.pdf
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Marc: |
LEADER 04075naa a2200337 a 4500
001 2151979
005 2023-02-28
008 2023 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3390/agriculture13020388$2DOI
100 1 $aVILELA, E. F.
245 $aNew spectral index and machine learning models for detecting coffee leaf miner infestation using sentinel-2 multispectral imagery.$h[electronic resource]
260 $c2023
300 $a17 p.
520 $aThe coffee leaf miner (Leucoptera coffeella) is a key coffee pest in Brazil that can cause severe defoliation and a negative impact on the productivity. Thus, it is essential to identify initial pest infestation for the sake of appropriate time control to avoid further economic damage to the coffee crops. A fast non-destructive method is an important tool that can be used to monitor the occurrence of the coffee leaf miner. The present work aims to identify the occurrence of coffee leaf miner infestation through a new vegetation index, using multispectral images from the Sentinel-2 satellite and the Google Earth Engine platform. Coffee leaf miner infestation was measured in the field in four cities in the state of Minas Gerais. The largest infestations occurred in September, October, and November but particularly in October 2021, in which the rate of infestation reached 85%, followed by September 2020 with a maximum infestation of 76%. The calculation steps of the vegetation indices and mappings were carried out in the Google Earth Engine cloud processing platform through the development of a script in JavaScript programming language. Combinations of two sensitive bands were selected to detect coffee leaf miner infestation, and from these, the 'Coffee-Leaf-Miner Index' was developed, which was compared with other existing vegetation indices in terms of their performance for coffee leaf miner detection. The combination of the NIR-BLUE and NIR-RED bands was more sensitive for the detection of coffee leaf miner infestation; therefore, the NIR, BLUE, and RED bands were selected to develop the new index. The 'Coffee-Leaf-Miner Index' presented the best performance among those evaluated, with a coefficient of determination of about 0.87, a root mean square error of 4.92% coffee leaf miner infestation, accuracy of 89.47%, and kappa coefficient of 95.39. The R2 range of other spectral indices which exist in the literature and which were used in this study was from 0.017 to 0.867, and the root mean square error ranged from 4.996 to 13.582% coffee leaf miner infestation. The machine learning method was then adopted using the supervised Random Forest and Support Vector Machine algorithms to recognize patterns of coffee leaf miner infestation in the field, only the Coffee-Leaf-Miner Index was used for the identification test of the coffee leaf miner infestation. The Support Vector Machine with linear Kernel type was applied to establish a discrimination model. The number of trees for the Random Forest classifier was 100. The Support Vector Machine presented a lower performance than the Random Forest algorithm, but the performance of both were above 80% for user and producer precision. Three bands (Blue, Red, NIR) were selected for the creation of the new index, which showed capacity for remote detection of coffee leaf miner infestation on a regional scale. Thus, 'Coffee-Leaf-Miner Index' can identify coffee leaf miner infestation thanks to all the complexity involved in detecting pests via orbital remote sensing.
650 $aApplication methods
650 $aPlant pests
650 $aRemote sensing
650 $aSpectral analysis
650 $aBicho Mineiro
650 $aInfestação
650 $aLeucoptera Coffeella
653 $aLeaf miner infestation
700 1 $aFERREIRA, W. P. M.
700 1 $aCASTRO, G. D. M. de
700 1 $aFARIA, A. L. R. de
700 1 $aLEITE, D. H.
700 1 $aLIMA, I. A.
700 1 $aMATOS, C. de S. M. de
700 1 $aSILVA, R. A.
700 1 $aVENZON, M.
773 $tAgriculture$gv.13, n. 2, 388, 2023.
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