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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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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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Registro original: |
Embrapa Café (CNPCa) |
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Biblioteca |
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Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
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| Registros recuperados : 104 | |
| 82. |  | MAY, A.; COELHO, L. F.; SILVA, E. H. F. M. da; VIANA, R. da S.; VIEIRA JUNIOR, N. A.; FERREIRA, W. P. M. Graphene: A new technology for agriculture. Research, Society and Development, v. 10, n. 2, e56610212827, 2021. p. 1-11.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 2 |
| Biblioteca(s): Embrapa Meio Ambiente. |
|    |
| 83. | | SILVA, M. A. V.; COSTA, J. M. N. da; ANDRADE, V. M. S. de; FERREIRA, W. P. M.; SANS, L. M. A.; OLIVEIRA, E. C. de. Desempenho do método de Penman-Monteith para estimativa do fluxo de vapor, em base horária, para a fase reprodutiva em uma cultura de milho. In: CONGRESSO BRASILEIRO DE METEOROLOGIA, 15., 2008, São Paulo. A meteorologia e a cidade: [anais]. São Paulo: SBMET. 2008.| Tipo: Artigo em Anais de Congresso / Nota Técnica |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 84. | | SILVA, M. A. V.; FERREIRA, W. P. M.; ANDRADE, V. M. S. de; COSTA, J. M. N. da; SANS, L. M. A.; OLIVEIRA, E. C. de. Desempenho do método de Penman-Monteith para estimativa do fluxo de vapor, em base horária, para a fase reprodutiva em uma cultura de milho. In: CONGRESSO NACIONAL DE MILHO E SORGO, 27.; SIMPOSIO BRASILEIRO SOBRE A LAGARTA-DO-CARTUCHO, SPODOPTERA FRUGIPERDA, 3.; WORKSHOP SOBRE MANEJO E ETIOLOGIA DA MANCHA BRANCA DO MILHO, 2008, Londrina. Agroenergia, produção de alimentos e mudanças climáticas: desafios para milho e sorgo: trabalhos e palestras. [Londrina]: IAPAR; [Sete Lagoas]: Embrapa Milho e Sorgo, 2008. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso / Nota Técnica |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 85. | | SILVA, M. A. V.; COSTA, J. M. N. da; ANDRADE, V. M. S. de; FERREIRA, W. P. M.; SANS, L. M. A.; OLIVEIRA, E. C. de. Eficiência de conversão da radiação fotossinteticamente ativa para a produção de fitomassa no milho BR 106. In: CONGRESSO BRASILEIRO DE METEOROLOGIA, 15., 2008, São Paulo. A meteorologia e a cidade: [anais]. São Paulo: SBMET, 2008. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso / Nota Técnica |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 86. | | NEVES, L. de O.; COSTA, J. M. N. da; COSTA, A. C. L. da; FERREIRA, W. P. M.; ANDRADE, V. M.; OLIVEIRA, F. A.; OLIVEIRA, E. C. de. Partição dos componentes do balanço de energia na cultura do fejião caupi (Vigna uniguiculata (L.). In: CONGRESSO BRASILEIRO DE AGROMETEOROLOGIA, 15., 2007, Aracaju. Efeito das mudanças climáticas na agricultura: anais... Aracaju: Sociedade Brasileira de Agrometeorologia: Embrapa Tabuleiros Costeiros, 2007. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso / Nota Técnica |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 87. | | FERREIRA, W. P. M.; RIBEIRO, S. M. N. F; FONSECA, H. P.; MIRANDA, T. V.; DIAS, C. R. G.; DI MARTINO, N. G. Profile of the women working in the coffee sector in Brazil (Phase 1). In: ARZABE, C.; MACIEIRA, J. C.; MENEZES, R. S. S.; BALIZA, D. P.; MOURÃO, T. F. (Ed.). Women in coffee in Brazil. Brasília, DF: Embrapa, 2018. E-book. il. color.| Tipo: Capítulo em Livro Técnico-Científico |
| Biblioteca(s): Embrapa Café. |
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| 88. | | JARAMILLO-GIRALDO, C.; FERREIRA, W. P. M.; FONSECA, H. P.; RIBEIRO, M. de F.; SILVA, L. M. R.; FERNANDES, R. B. A. Relationship Between Spatio-Temporal Leaf Area Index and Crop Coefficient When Monitoring Coffee Plots in Brazil. Journal of Agricultural Science, v. 11, n. 15, p, 187-199, 2019.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Café. |
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| 90. | | FERREIRA, W. P. M.; FERREIRA, G. R.; BARBOSA, T. K. M.; RIBEIRO, M. F.; FERNANDO FILHO, E. I.; RUFINO, J. L. Região das Matas de Minas tem clima ideal para produção de café. Cafeicultura, v.14, n. 16, p. 10-13, 2016.| Tipo: Artigo de Divulgação na Mídia |
| Biblioteca(s): Embrapa Café. |
|  |
| 91. | | OLIVEIRA, E. C. de; COSTA, J. M. N. da; PAULA JÚNIOR, T. J. de; FERREIRA, W. P. M.; NEVES, L. O. Uso do modelo CROPGRO-Drybean na determinação das melhores datas de semeadura da "seca" para a região de Viçosa, MG. In: CONGRESSO BRASILEIRO DE AGROMETEOROLOGIA, 17., 2011, Guarapari. Riscos climáticos e cenários agrícolas futuros: anais... Guarapari: Incaper, 2011.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 93. | | SILVA, M. A. V.; COSTA, J. M. N. da; ANDRADE, V. M. S. de; FERREIRA, W. P. M.; OLIVEIRA, E. C. de. Semeadura do milho para a região de Barreiras, BA, baseada na probabilidade de ocorrência de periódos secos e chuvosos. In: CONGRESSO BRASILEIRO DE METEOROLOGIA, 15., 2008, São Paulo. A meteorologia e a cidade: [anais]. São Paulo: SBMET, 2008. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso / Nota Técnica |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 94. | | SILVEIRA, A. de S.; PINHEIRO, A. C. T.; FERREIRA, W. P. M.; SILVA, L. J. da; RUFINO, J. L. dos S.; SAKIYAMA, N. S. Sensory analysis of specialty coffee from different environmental conditions in the region of Matas de Minas, Minas Gerais, Brazil. Revista Ceres, Viçosa, v.63, n.4, p. 436-443. jul/ago. 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Café. |
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| 95. | | SILVA, L. M. R.; RIBEIRO, M. de F.; FERREIRA, W. P. M.; ROCHA JUNIOR, P. R. da; FERNANDES, R. B. A. Water footprint of Arabica coffee from "Matas de Minas" under shade management. Revista Ceres, Viçosa, v. 69, n.4, p. 488-494, jul/ago, 2022.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Café. |
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| 96. | | SILVA JÚNIOR, J. L. C. da; GOIS, G. de; FERREIRA, W. P. M.; COSTA, L. C.; SILVA, A. F. da; AMORIM, M. C. de; ROSIGNOLI, J.; ELEUTÉRIO, M. M. Avaliação da ocorrencia de secas no município de Viçosa-MG, utilizando o índice de precipitação padronizada (SPI). In: CONGRESSO BRASILEIRO DE AGROECOLOGIA, 14., Campinas. Anais eletrônicos... Campinas: Sociedade Brasileira de Agrometeorologia, 2005. 2 p. 2 p. 1 CD-ROM| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Territorial. |
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| 97. | | NEVES, L. de O.; COSTA, J. M. N. da; ROBERTI, D. R.; JUSTINO, F. B.; FERREIRA, W. P. M.; OLIVEIRA, E. C. de; AGUIAR, L. J. G.; GONÇALVES, P. H.; HIGEL, H. F.; RODRIGUES, M. P. Estimativa do balanço de energia utilizando o modelo de interação biosfera-atmosfera - CABLE. In: CONGRESSO BRASILEIRO DE AGROMETEOROLOGIA, 17., 2011, Guarapari. Riscos climáticos e cenários agrícolas futuros: anais... Guarapari: Incaper, 2011. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 98. | | OLIVEIRA, L. A.; SOUZA, G. A. de; SILVA, B. T.; ROCHA, A. A. G.; PICOLI, E. A. de T.; PEREIRA, D. de S.; DONZELES, S. M. L.; RIBEIRO, M. de F.; FERREIRA, W. P. M. Histochemical approach of the mobilization of reserve compounds in germinating coffee seeds. Coffee Science, v. 15, e151704, p. 77 -100, 2020.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Café. |
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| 99. | | SILVA, M. A. V.; FERREIRA, W. P. M.; ANDRADE, W. M. S. de; COSTA, J. M. N. da; NEVES, L. de O.; OLIVEIRA, E. C. de; GONÇALVES, P. H.; MOSCON, E. S.; BEZERRA, A. R. G. Influência da área foliar na assimilação de CO2 ao longo dos estádios fenológicos em um dossel de milho. In: CONGRESSO BRASILEIRO DE AGROMETEOROLOGIA, 17., 2011, Guarapari. Riscos climáticos e cenários agrícolas futuros: anais... Guarapari: Incaper, 2011. 1 CD-ROM.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 100. | | MAY, A.; FERREIRA, W. P. M.; VIANA, R. da S.; VILELA, E. S. D.; SILVA, E. H. F. M. da; VIEIRA JUNIOR, N. A.; HIRANO, R. T.; SANTOS, M. de S. dos; MOREIRA, B. R. de A. Development of coffee plant under different doses of an amino acid-based organomineral product. International Journal of Science and Research, v. 9, n. 5, p. 1267-1271, May 2020.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: C - 0 |
| Biblioteca(s): Embrapa Café; Embrapa Meio Ambiente. |
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| Registros recuperados : 104 | |
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| Nenhum registro encontrado para a expressão de busca informada. |
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