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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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Registro original: |
Embrapa Instrumentação (CNPDIA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Agropecuária Oeste. |
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Data corrente: |
22/01/2024 |
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Data da última atualização: |
22/01/2024 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
PATRONE, F. P. P.; RAMOS, F. da S.; FERREIRA, R. S.; MELO, P. I.; SALTON, J. C.; COMUNELLO, E.; TOMAZI, M. |
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Afiliação: |
FLÁVIA PRISCILA PINHEIRO PATRONE, ESTUDANTE DE GRADUAÇÃO, CENTRO UNIVERSITÁRIO DA GRANDE DOURADOS, BOLSISTA, INICIAÇÃO CIENTÍFICA, CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO; FABRÍCIA DA SILVA RAMOS, ENGENHEIRA-AGRÔNOMA, DOUTORA EM AGRONOMIA, BOLSISTA, INOVAÇÃO TECNOLÓGICA, FUNDAÇÃO DE APOIO À PESQUISA E AO DESENVOLVIMENTO; RAFAEL SILVA FERREIRA, ENGENHEIRO-AGRÔNOMO, DOUTOR EM AGRONOMIA, BOLSISTA, INOVAÇÃO TECNOLÓGICA, FUNDAÇÃO DE APOIO À PESQUISA E AO DESENVOLVIMENTO; PABLO INÁCIO MELO, ESTUDANTE DE GRADUAÇÃO, CENTRO UNIVERSITÁRIO DA GRANDE DOURADOS, BOLSISTA, INICIAÇÃO CIENTÍFICA, CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO; JULIO CESAR SALTON, CPAO; EDER COMUNELLO, CPAO; MICHELY TOMAZI, CPAO. |
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Título: |
Resistência à penetração do solo sob diferentes sistemas de manejo, Dourados, MS. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: JORNADA DE INICIAÇÃO À PESQUISA DA EMBRAPA, 2023, Dourados. Resumos... Dourados: Embrapa Agropecuária Oeste, 2023. (Embrapa Agropecuária Oeste. Eventos técnicos & científicos, 2). p. 28; JIPE 2023. |
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Idioma: |
Português |
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Thesagro: |
Compactação do Solo; Umidade do Solo. |
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Categoria do assunto: |
A Sistemas de Cultivo |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1161135/1/28.pdf
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Marc: |
LEADER 00761nam a2200193 a 4500
001 2161135
005 2024-01-22
008 2023 bl uuuu u00u1 u #d
100 1 $aPATRONE, F. P. P.
245 $aResistência à penetração do solo sob diferentes sistemas de manejo, Dourados, MS.$h[electronic resource]
260 $aIn: JORNADA DE INICIAÇÃO À PESQUISA DA EMBRAPA, 2023, Dourados. Resumos... Dourados: Embrapa Agropecuária Oeste, 2023. (Embrapa Agropecuária Oeste. Eventos técnicos & científicos, 2). p. 28; JIPE 2023.$c2023
650 $aCompactação do Solo
650 $aUmidade do Solo
700 1 $aRAMOS, F. da S.
700 1 $aFERREIRA, R. S.
700 1 $aMELO, P. I.
700 1 $aSALTON, J. C.
700 1 $aCOMUNELLO, E.
700 1 $aTOMAZI, M.
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