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Registro Completo |
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Biblioteca(s): |
Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
12/03/2008 |
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Data da última atualização: |
15/05/2024 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
SOUZA, D. S. L.; PIRES, N. F.; SOUSA, B. A.; GROSSI DE SÁ, M.; ROMANO, E.; ROCHA, T. L.; OLIVEIRA, G. R.; BARBOSA, A. E. A. D.; TEIXEIRA, F. M.; MARTINS DE SÁ, C.; FRAGOSO, R. R.; MURAD, A. M.; SILVA, M. C. M.; GROSSI DE SÁ, M. F. |
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Afiliação: |
DJAIR DOS SANTOS DE LIMA E SOUZA; NATÁLIA FAUSTINO PIRES; BRUNA ARAUJO DE SOUSA; MAÍRA GROSSI DE SÁ; EDUARDO ROMANO DE CAMPOS, EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA; THALES LIMA ROCHA, EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA; GUSTAVO RAMOS DE OLIVEIRA; AULUS ESTEVÃO ANJOS DE DEUS BARBOSA; FABIANO DE MOUTA TEIXEIRA; C. MARTINS DE SÁ, UNIVERSIDADE DE BRASÍLIA; RODRIGO DA ROCHA FRAGOSO; ALINE MELRO MURAD; MARIA CRISTINA MATTAR DA SILVA, EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA; MARIA FÁTIMA GROSSI DE SA, EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA. |
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Título: |
Expressão de dsRNAs de gene específico de glândula esofagiana em plantas transgênicas de Nicotiana tabaccum conferem alta resistência à Meloidogyne incognita. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
In: CONGRESSO BRASILEIRO DE GENÉTICA, 53., 2007, Águas de Lindóia. Resumos... Águas de Lindóia: Sociedade Brasileira de Genética, 2007. |
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Páginas: |
p. 186 |
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Idioma: |
Português |
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Palavras-Chave: |
Nicotiana tabaccum. |
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Thesagro: |
Meloidogyne Incognita; Planta Transgênica. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01037nam a2200301 a 4500
001 1180839
005 2024-05-15
008 2007 bl uuuu u01u1 u #d
100 1 $aSOUZA, D. S. L.
245 $aExpressão de dsRNAs de gene específico de glândula esofagiana em plantas transgênicas de Nicotiana tabaccum conferem alta resistência à Meloidogyne incognita.
260 $aIn: CONGRESSO BRASILEIRO DE GENÉTICA, 53., 2007, Águas de Lindóia. Resumos... Águas de Lindóia: Sociedade Brasileira de Genética$c2007
300 $ap. 186
650 $aMeloidogyne Incognita
650 $aPlanta Transgênica
653 $aNicotiana tabaccum
700 1 $aPIRES, N. F.
700 1 $aSOUSA, B. A.
700 1 $aGROSSI DE SÁ, M.
700 1 $aROMANO, E.
700 1 $aROCHA, T. L.
700 1 $aOLIVEIRA, G. R.
700 1 $aBARBOSA, A. E. A. D.
700 1 $aTEIXEIRA, F. M.
700 1 $aMARTINS DE SÁ, C.
700 1 $aFRAGOSO, R. R.
700 1 $aMURAD, A. M.
700 1 $aSILVA, M. C. M.
700 1 $aGROSSI DE SÁ, M. F.
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Registro original: |
Embrapa Recursos Genéticos e Biotecnologia (CENARGEN) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
16/11/2021 |
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Data da última atualização: |
09/06/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
FURUYA, D. E. G.; MA, L.; PINHEIRO, M. M. F.; GOMES, F. D. G.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; RODRIGUES, D. de C.; BLASSIOLI- MORAES, M. C.; MICHEREFF, M. F. F.; BORGES, M.; ALAUMANN, R. A.; FERREIRA, E. J.; OSCO, L. P.; RAMOS, A. P. M.; LI, J.; JORGE, L. A. de C. |
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Afiliação: |
MARIA CAROLINA BLASSIOLI MORAES, Cenargen; MIGUEL BORGES, Cenargen; EDNALDO JOSE FERREIRA, CNPDIA; LUCIO ANDRE DE CASTRO JORGE, CNPDIA. |
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Título: |
Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
International Journal of Applied Earth Observation and Geoinformation, v. 105, 102608, 2021. |
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Páginas: |
1 - 10 |
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ISSN: |
0303-2434 |
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DOI: |
https://doi.org/10.1016/j.jag.2021.102608 |
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Idioma: |
Inglês |
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Conteúdo: |
Accurately detecting the insect damage caused in plants might reduce losses in crop yields. Hyperspectral data is a well-accepted data source to attend this issue. However, due to their high dimensional, both robust and intelligent methods are required to extract information from these datasets. Therefore, we explore the processing of hyperspectral data with artificial intelligence methods joined with clustering techniques to detect insect herbivory damage in maize plants. We measured the leaf spectral response from three different groups of maize plants: control (undamaged plants); damaged by Spodoptera frugiperda herbivory, and damaged by Dichelops meiacanthus. Data were collected with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We adjusted eight machine learning methods. We also determined the most contributive wavelengths to differentiate undamaged from damaged plants by insect herbivore attack using clustering strategy. For that, we applied the clusterization method based on a self-organizing map (SOM). The Random Forest (RF) model is the overall best learner, and up to the 5th day of analysis represents the most adequate day to segregate maize undamaged from damaged maize. RF was able to separate the three groups of treatments with an F1-measure of up to 96.7% (Recall of 96.7% and Precision of 96.7%). Additionally, we found out that the most representative spectral regions are located in the near-infrared range. Our approach consists of an original contribution to early differentiate the undamaged plant from the damaged one due to insect-attack, highlighting the most contributive wavelengths to map this occurrence. MenosAccurately detecting the insect damage caused in plants might reduce losses in crop yields. Hyperspectral data is a well-accepted data source to attend this issue. However, due to their high dimensional, both robust and intelligent methods are required to extract information from these datasets. Therefore, we explore the processing of hyperspectral data with artificial intelligence methods joined with clustering techniques to detect insect herbivory damage in maize plants. We measured the leaf spectral response from three different groups of maize plants: control (undamaged plants); damaged by Spodoptera frugiperda herbivory, and damaged by Dichelops meiacanthus. Data were collected with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We adjusted eight machine learning methods. We also determined the most contributive wavelengths to differentiate undamaged from damaged plants by insect herbivore attack using clustering strategy. For that, we applied the clusterization method based on a self-organizing map (SOM). The Random Forest (RF) model is the overall best learner, and up to the 5th day of analysis represents the most adequate day to segregate maize undamaged from damaged maize. RF was able to separate the three groups of treatments with an F1-measure of up to 96.7% (Recall of 96.7% and Precision of 96.7%). Additionally, we found out that the most representative spectral regions are located in the near-infrared range. Our approach consis... Mostrar Tudo |
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Palavras-Chave: |
Proximal hyperspectral sensing; Random forest. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02800naa a2200361 a 4500
001 2136152
005 2022-06-09
008 2021 bl uuuu u00u1 u #d
022 $a0303-2434
024 7 $ahttps://doi.org/10.1016/j.jag.2021.102608$2DOI
100 1 $aFURUYA, D. E. G.
245 $aPrediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data.$h[electronic resource]
260 $c2021
300 $a1 - 10
520 $aAccurately detecting the insect damage caused in plants might reduce losses in crop yields. Hyperspectral data is a well-accepted data source to attend this issue. However, due to their high dimensional, both robust and intelligent methods are required to extract information from these datasets. Therefore, we explore the processing of hyperspectral data with artificial intelligence methods joined with clustering techniques to detect insect herbivory damage in maize plants. We measured the leaf spectral response from three different groups of maize plants: control (undamaged plants); damaged by Spodoptera frugiperda herbivory, and damaged by Dichelops meiacanthus. Data were collected with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We adjusted eight machine learning methods. We also determined the most contributive wavelengths to differentiate undamaged from damaged plants by insect herbivore attack using clustering strategy. For that, we applied the clusterization method based on a self-organizing map (SOM). The Random Forest (RF) model is the overall best learner, and up to the 5th day of analysis represents the most adequate day to segregate maize undamaged from damaged maize. RF was able to separate the three groups of treatments with an F1-measure of up to 96.7% (Recall of 96.7% and Precision of 96.7%). Additionally, we found out that the most representative spectral regions are located in the near-infrared range. Our approach consists of an original contribution to early differentiate the undamaged plant from the damaged one due to insect-attack, highlighting the most contributive wavelengths to map this occurrence.
653 $aProximal hyperspectral sensing
653 $aRandom forest
700 1 $aMA, L.
700 1 $aPINHEIRO, M. M. F.
700 1 $aGOMES, F. D. G.
700 1 $aGONÇALVEZ, W. N.
700 1 $aMARCATO JUNIOR, J.
700 1 $aRODRIGUES, D. de C.
700 1 $aBLASSIOLI- MORAES, M. C.
700 1 $aMICHEREFF, M. F. F.
700 1 $aBORGES, M.
700 1 $aALAUMANN, R. A.
700 1 $aFERREIRA, E. J.
700 1 $aOSCO, L. P.
700 1 $aRAMOS, A. P. M.
700 1 $aLI, J.
700 1 $aJORGE, L. A. de C.
773 $tInternational Journal of Applied Earth Observation and Geoinformation$gv. 105, 102608, 2021.
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