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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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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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Embrapa Instrumentação (CNPDIA) |
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| Registros recuperados : 7 | |
| 1. |  | THEODORO, J. M. V.; MARTINEZ, O. D. M.; TOLEDO, R. C. L.; LISBOA, P. C.; MARTINS, A. M. D.; CARVALHO, C. W. P. de; MARTINO, H. S. D. Milheto germinado (Pennisetum glaucum (L.) reduz alterações hepática e renal e não altera hormônios tireoidianos em ratos. In: CONGRESSO ON-LINE BRASILEIRO DE TECNOLOGIA DE CEREAIS E PANIFICAÇÃO, 2020, virtual. Anais... Saudabilidade na Indústria de Cereais e In: CONGRESSO ON-LINE BRASILEIRO DE TECNOLOGIA DE CEREAIS E PANIFICAÇÃO, 2020, virtual. Anais... Saudabilidade na Indústria de Cereais e Panificação. Sete Lagoas: Universidade Federal de São João del-Rei, 2020. 4 p. CBCP; 5 a 9 out.| Biblioteca(s): Embrapa Agroindústria de Alimentos. |
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| 2. | | THEODORO, J. M. V.; MARTINEZ, O. D. M.; GRANCIERI, M.; TOLEDO, R. C. L.; BINOTI, M. L.; MARTINS, A. M. D.; CARVALHO, C. W. P. de; LISBOA, P. C.; MARTINO, H. S. Germinated millet flour (Pennisetum Glaucum (L.) R. BR.) improves adipogenesis and glucose metabolism and maintains thyroid function in vivo. Food & Function, v. 12, p. 6083-6090, 2021.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
| Biblioteca(s): Embrapa Agroindústria de Alimentos. |
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| 3. | | FERREIRA, M. S.; VIEIRA, A. M.; AGUIAR, A. S. De; SABARENSE, C. M.; MOURA, E. G.; LOPES, F. C. F.; TOLEDO, F.; MATHEUS, K.; ALMEIDA, M, M. De; FIGUEIREDO, M. S.; LISBÔA, P. C.; SILVA, P. H. F. Da; BRASIEL, P. G.; LUQUETTI, C. P. D. O consumo de soja por Ratas Wistar na lactação programa parâmetros metabólicos na progênie adulta In: REUNIÃO ANUAL DA FEDERAÇÃO DE SOCIEDADES DE BIOLOGIA EXPERIMENTAL, 31., 2016, Foz do Iguaçu. Anais... São Paulo: Federação de Sociedade de Biologia Experimental, 2016.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 4. | | VIEIRA, A. M.; BRASIEL, P. G. de A.; FERREIRA, M. S.; MATEUS, K.; FIGUEIREDO, M. S.; LISBOA, P. C.; MOURA, E. G. de; CORRÊA, J. O. do A.; LOPES, F. C. F.; SILVA, P. H. F. da; SABARENSE, C. M.; DUTRA, S. C. P. L.; AGUIAR, A. S. de. Maternal soybean diet during lactation alters breast milk composition and programs the lipid profile in adult male rat offspring. Endocrine, v. 60, n. 2, p. 272-281, 2018.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 5. | | ALMEIDA, M. M.; SOUZA, F. F.; SOUZA, Y. O.; REZENDE, C. O.; CONCEIÇÃO, E. P. S.; LISBOA, P. C.; MOURA, E. G.; LUQUETTI, S. C. P. D.; SABARENSE, C. M.; GAMA, M. A. S. da; LOPES, F. C. F.; GARCIA, R. M. G. Mistura sintética de ácido linoléico conjugado CIS-9 Trans-11 e Trans-10 CIS-12 diminui níveis da proteína PPARγ no tecido adiposo de ratos Wistar machos adultos. In: REUNIÃO ANUAL DA FeSBE, 29., 2014, Caxambu. Anais... Caxambu: FeSBE, 2014.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 6. | | ALMEIDA, M. M. de; LUQUETTI, S. C. P. D.; SABARENSE, C. M.; CORRÊA, J. O. do A.; REIS, L. G. dos; CONCEIÇÃO, E. P. S. da; LISBOA, P. C.; MOURA, E. G. de; GAMEIRO, J.; GAMA, M. A. S. da; LOPES, F. C. F.; GARCIA, R. M. G. Butter naturally enriched in cis-9, trans-11 CLA prevents hyperinsulinemia and increases both serum HDL cholesterol and triacylglycerol levels in rats. Lipids in Health and Disease, v. 13, article 200, 2014.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
| Biblioteca(s): Embrapa Gado de Leite. |
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| 7. | | ALMEIDA, M. M. de; SOUZA, Y. O. de; LUQUETTI, S. C. P. D.; SABARENSE, C. M.; CORRÊA, J. O. do A.; CONCEIÇÃO, E. P. S. da; LISBOA, P. C.; MOURA, E. G. de; SOARES, S. M. A.; GUALBERTO, A. C. M.; GAMEIRO, J.; GAMA, M. A. S. da; LOPES, F. C. F.; GARCIA, R. M. G. Cis-9, trans-11 and trans-10, cis-12 CLA mixture does not change body composition, induces insulin resistance and increases serum HDL cholesterol level in rats. Journal of Oleo Science, v. 64, n. 5, p. 539-551, 2015.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Gado de Leite. |
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| Registros recuperados : 7 | |
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