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Registro Completo |
Biblioteca(s): |
Embrapa Instrumentação. |
Data corrente: |
16/11/2021 |
Data da última atualização: |
09/06/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
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. |
Afiliação: |
MARIA CAROLINA BLASSIOLI MORAES, Cenargen; MIGUEL BORGES, Cenargen; EDNALDO JOSE FERREIRA, CNPDIA; LUCIO ANDRE DE CASTRO JORGE, CNPDIA. |
Título: |
Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
International Journal of Applied Earth Observation and Geoinformation, v. 105, 102608, 2021. |
Páginas: |
1 - 10 |
ISSN: |
0303-2434 |
DOI: |
https://doi.org/10.1016/j.jag.2021.102608 |
Idioma: |
Inglês |
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 |
Palavras-Chave: |
Proximal hyperspectral sensing; Random forest. |
Categoria do assunto: |
-- |
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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| Acesso ao texto completo restrito à biblioteca da Embrapa Pecuária Sul. Para informações adicionais entre em contato com cppsul.biblioteca@embrapa.br. |
Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Pecuária Sul; Embrapa Recursos Genéticos e Biotecnologia. |
Data corrente: |
17/07/2015 |
Data da última atualização: |
10/03/2016 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
CARDOSO, F. F.; GOMES, C. C. G.; SOLLERO, B. P.; OLIVEIRA, M. M.; ROSO, V. M.; PICCOLI, M. L.; HIGA, R. H.; YOKOO, M. J.; CAETANO, A. R.; AGUILLAR, I. |
Afiliação: |
ROBERTO HIROSHI HIGA, CNPTIA. |
Título: |
Genomic prediction for tick resistance in Braford and Hereford cattle. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
Journal of Animal Science, v. 93, n. 6, p. 2693-2705, June 2015. |
Idioma: |
Inglês |
Conteúdo: |
Phenotype, Genotype, and Pedigree Data. Statistical Models and Analyses. Cross-Validation and Prediction Accuracy. Breed Group Means and Variance Components for Tick Count. Pedigree Correction and Reconstruction. Pedigree Correction and Reconstruction. Clustering for Cross-Validation. Accuracy and Bias of Direct Genomic Predictions. |
Palavras-Chave: |
Seleção genômica. |
Thesagro: |
Bovino; Genótipo. |
Thesaurus NAL: |
animal health; Genomics; Genotype; marker-assisted selection. |
Categoria do assunto: |
-- X Pesquisa, Tecnologia e Engenharia |
Marc: |
LEADER 01171naa a2200313 a 4500 001 2028976 005 2016-03-10 008 2015 bl uuuu u00u1 u #d 100 1 $aCARDOSO, F. F. 245 $aGenomic prediction for tick resistance in Braford and Hereford cattle. 260 $c2015 520 $aPhenotype, Genotype, and Pedigree Data. Statistical Models and Analyses. Cross-Validation and Prediction Accuracy. Breed Group Means and Variance Components for Tick Count. Pedigree Correction and Reconstruction. Pedigree Correction and Reconstruction. Clustering for Cross-Validation. Accuracy and Bias of Direct Genomic Predictions. 650 $aanimal health 650 $aGenomics 650 $aGenotype 650 $amarker-assisted selection 650 $aBovino 650 $aGenótipo 653 $aSeleção genômica 700 1 $aGOMES, C. C. G. 700 1 $aSOLLERO, B. P. 700 1 $aOLIVEIRA, M. M. 700 1 $aROSO, V. M. 700 1 $aPICCOLI, M. L. 700 1 $aHIGA, R. H. 700 1 $aYOKOO, M. J. 700 1 $aCAETANO, A. R. 700 1 $aAGUILLAR, I. 773 $tJournal of Animal Science$gv. 93, n. 6, p. 2693-2705, June 2015.
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