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![](/consulta/web/img/deny.png) | Acesso ao texto completo restrito à biblioteca da Embrapa Recursos Genéticos e Biotecnologia. Para informações adicionais entre em contato com cenargen.biblioteca@embrapa.br. |
Registro Completo |
Biblioteca(s): |
Embrapa Instrumentação; Embrapa Recursos Genéticos e Biotecnologia. |
Data corrente: |
10/09/2021 |
Data da última atualização: |
03/10/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
RAMOS, A. P. M.; GOMES, F. D. G.; PINHEIRO, M. M. F.; FURUYA, D. E. G.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; MICHEREFF, M. F. F.; MORAES, M. C. B.; BORGES, M.; LAUMANN, R. A.; LIESENBERG, V.; JORGE, L. A. de C.; OSCO, L. P. |
Afiliação: |
ANA PAULA MARQUES RAMOS, UNOESTE; FELIPE DAVID GEORGES GOMES, UNOESTE; MAYARA MAEZANO FAITA PINHEIRO, UNOESTE; DANIELLE ELIS GARCIA FURUYA, UNOESTE; WESLEY NUNES GONÇALVEZ, UFMS; JOSÉ MARCATO JUNIOR, UFMS; MIRIAN FERNANDES FURTADO MICHEREFF; MARIA CAROLINA BLASSIOLI MORAES, Cenargen; MIGUEL BORGES, Cenargen; RAUL ALBERTO LAUMANN, Cenargen; VERALDO LIESENBERG, Udesc; LUCIO ANDRE DE CASTRO JORGE, CNPDIA; LUCAS PRADO OSCO, UNOESTE. |
Título: |
Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
Precision Agriculture, 2021. |
DOI: |
https://doi.org/10.1007/s11119-021-09845-4 |
Idioma: |
Inglês |
Notas: |
Na publicação: Maria Carolina Blassioli-Moraes; Raúl Alberto Alaumann. |
Conteúdo: |
ABSTRACT: The Spodoptera frugiperda (i.e., fall armyworm) causes irreversible damage in cotton cultivars, and its visual inspection on plants is a burdensome task for humans. A recent strategy to automatically do similar tasks is processing hyperspectral reflectance measurements with machine learning algorithms. Herein, its proposed a framework for modeling the spectral response of cotton plants under the fall armyworm attacks using machine learning algorithms, culminating in a theoretical model creation based on the band simulation process. A controlled experiment was conducted to collect hyperspectral radiance measurements from health and damage cotton plants over eight days. A hand-held spectroradiometer operating from 350 to 2500 nm was used. Several algorithms were evaluated, and a ranking approach was adopted to identify the most contributive wavelengths for detecting the damage. The Self-Organizing Map method was applied to organize the spectral wavelengths into groups, favoring the theoretical model creation for two sensors: OLI (Landsat-8) and MSI (Sentinel-2). It was found that the Random Forest algorithm produced the most suitable model, and the last day of analysis was better to separate healthy and damaged plants (F-measure: 0.912). The best spectral regions range from the red to near-infrared (650 to 1350 nm) and the shortwave infrared (1570 to 1640 nm). The theoretical model returned accurate results using both sensors (OLI, F-Measure?=?0.865, and MSI, F-Measure?=?0.886). In conclusion, the proposed framework contributes to accurately identifying cotton plants under the Spodoptera frugiperda attack for both hyperspectral and multispectral scales. MenosABSTRACT: The Spodoptera frugiperda (i.e., fall armyworm) causes irreversible damage in cotton cultivars, and its visual inspection on plants is a burdensome task for humans. A recent strategy to automatically do similar tasks is processing hyperspectral reflectance measurements with machine learning algorithms. Herein, its proposed a framework for modeling the spectral response of cotton plants under the fall armyworm attacks using machine learning algorithms, culminating in a theoretical model creation based on the band simulation process. A controlled experiment was conducted to collect hyperspectral radiance measurements from health and damage cotton plants over eight days. A hand-held spectroradiometer operating from 350 to 2500 nm was used. Several algorithms were evaluated, and a ranking approach was adopted to identify the most contributive wavelengths for detecting the damage. The Self-Organizing Map method was applied to organize the spectral wavelengths into groups, favoring the theoretical model creation for two sensors: OLI (Landsat-8) and MSI (Sentinel-2). It was found that the Random Forest algorithm produced the most suitable model, and the last day of analysis was better to separate healthy and damaged plants (F-measure: 0.912). The best spectral regions range from the red to near-infrared (650 to 1350 nm) and the shortwave infrared (1570 to 1640 nm). The theoretical model returned accurate results using both sensors (OLI, F-Measure?=?0.865, and MSI, F-Measu... Mostrar Tudo |
Palavras-Chave: |
Insect damage; Machine learning; Spectral data; Theoretical model. |
Categoria do assunto: |
-- |
Marc: |
LEADER 02768naa a2200337 a 4500 001 2138144 005 2023-10-03 008 2021 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1007/s11119-021-09845-4$2DOI 100 1 $aRAMOS, A. P. M. 245 $aDetecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements.$h[electronic resource] 260 $c2021 500 $aNa publicação: Maria Carolina Blassioli-Moraes; Raúl Alberto Alaumann. 520 $aABSTRACT: The Spodoptera frugiperda (i.e., fall armyworm) causes irreversible damage in cotton cultivars, and its visual inspection on plants is a burdensome task for humans. A recent strategy to automatically do similar tasks is processing hyperspectral reflectance measurements with machine learning algorithms. Herein, its proposed a framework for modeling the spectral response of cotton plants under the fall armyworm attacks using machine learning algorithms, culminating in a theoretical model creation based on the band simulation process. A controlled experiment was conducted to collect hyperspectral radiance measurements from health and damage cotton plants over eight days. A hand-held spectroradiometer operating from 350 to 2500 nm was used. Several algorithms were evaluated, and a ranking approach was adopted to identify the most contributive wavelengths for detecting the damage. The Self-Organizing Map method was applied to organize the spectral wavelengths into groups, favoring the theoretical model creation for two sensors: OLI (Landsat-8) and MSI (Sentinel-2). It was found that the Random Forest algorithm produced the most suitable model, and the last day of analysis was better to separate healthy and damaged plants (F-measure: 0.912). The best spectral regions range from the red to near-infrared (650 to 1350 nm) and the shortwave infrared (1570 to 1640 nm). The theoretical model returned accurate results using both sensors (OLI, F-Measure?=?0.865, and MSI, F-Measure?=?0.886). In conclusion, the proposed framework contributes to accurately identifying cotton plants under the Spodoptera frugiperda attack for both hyperspectral and multispectral scales. 653 $aInsect damage 653 $aMachine learning 653 $aSpectral data 653 $aTheoretical model 700 1 $aGOMES, F. D. G. 700 1 $aPINHEIRO, M. M. F. 700 1 $aFURUYA, D. E. G. 700 1 $aGONÇALVEZ, W. N. 700 1 $aMARCATO JUNIOR, J. 700 1 $aMICHEREFF, M. F. F. 700 1 $aMORAES, M. C. B. 700 1 $aBORGES, M. 700 1 $aLAUMANN, R. A. 700 1 $aLIESENBERG, V. 700 1 $aJORGE, L. A. de C. 700 1 $aOSCO, L. P. 773 $tPrecision Agriculture, 2021.
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10. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | CONCEIÇÃO, M. A. F.; ARAÚJO, W. F.; TONIETTO, J.; PRADO, R. J. do. Aptidão climática para o cultivo da videira em Boa Vista, Roraima. Climatic conditions for grapevine cultivation in Boa Vista, Roraima, Brazil Revista Agro@mbiente On-line, v. 7, n. 3, p. 277-283, set.-dez. 2013Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
Biblioteca(s): Embrapa Uva e Vinho. |
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11. | ![Imagem marcado/desmarcado](/consulta/web/img/desmarcado.png) | ANTUNES, F.; SILVA, D. C. O. da; ARAÚJO, W. F.; CHAGAS, E. A.; COUCEIRO, M. A. Efeito da ventilação do frasco e concentração da sacarose no cultivo in vitro de banana (musa sp.). In: CONGRESSO BRASILEIRO DE FRUTICULTURA, 21., 2010, Natal. Frutas: saúde, inovação e responsabilidade: anais. Natal: SBF, 2010.Tipo: Artigo em Anais de Congresso |
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