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| Registros recuperados : 13 | |
| 5. |  | OSCO, L. P.; ARRUDA, M. S.; GONÇALVES, D. N.; DIAS, A.; BATISTOTI, J.; SOUZA, M.; GOMES, F. D. G.; RAMOS, A. P. M.; JORGE, L. A. de C.; LIESENBERG, V.; LI, J.; MA, L.; MARCATO JUNIOR, J.; GONÇALVES, W. N. A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery. ISPRS Journal of Photogrammetry and Remote Sensing, v. 174, 2021. 1 - 17| Biblioteca(s): Embrapa Instrumentação. |
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| 6. | | OSCO, L. P.; MARCATO JUNIOR, J.; RAMOS, A. P. M.; JORGE, L. A. de C.; FATHOLAHI, S. N.; SILVA, J. A.; MATSUBARA, E. T.; PISTORI, H.; GONÇALVES, W. N.; LI, J. A review on deep learning in UAV remote sensing. International Journal of Applied Earth Observations and Geoinformation, v. 102, 102456, 2021. 1 - 22| Biblioteca(s): Embrapa Instrumentação. |
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| 7. | | OSCO, L. P.; NOGUEIRA, K.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; FURUYA, D. E. G.; GONÇALVES, W. N.; JORGE, L. A. de C.; MARCATO JUNIOR, J.; SANTOS, J. A. Semantic segmentation of citrus-orchard using deep neural networks and multispectral UAV-based imagery. Precision Agriculture, v. 22, n. 4,2021. 1171-1188| Biblioteca(s): Embrapa Instrumentação. |
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| 8. | | 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. Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements. Precision Agriculture, 2021. Na publicação: Maria Carolina Blassioli-Moraes; Raúl Alberto Alaumann.| Biblioteca(s): Embrapa Instrumentação; Embrapa Recursos Genéticos e Biotecnologia. |
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| 9. | | OSCO, L. P.; FURUYA, D. E. G.; FURUYA, M. T. G.; CORRÊA, D. V.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; BORGES, M.; BLASSIOLI-MORAES, M. C.; MICHEREFF, M. F. F.; AQUUINO, M. F. S.; LAUMANN, R. A.; LISENBERG, V.; RAMOS, A. P. M.; JORGE, L. A. de C. An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods. Infrared Physics & Technology, v. 123, 104203, 2022. 13 p.| Biblioteca(s): Embrapa Instrumentação. |
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| 10. | | OSCO, L. P.; FURUYA, D. E. G.; FURUYA, M. T. G.; CORRÊA, D. V.; GONÇALVEZ, W. N.; MARCATO JUNIOR, J.; BORGES, M.; MORAES, M. C. B.; MICHEREFF, M. F. F.; AQUINO, M. F. S.; LAUMANN, R. A.; LISENBERG, V.; RAMOS, A. P. M.; JORGE, L. A. de C. An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods. Infrared Physics & Technology, v. 123, 2022. 104203. Na publicação: Maria Carolina Blassioli-Moraes.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 11. | | OSCO, L. P.; RAMOS, A. P. M.; PINHEIRO, M. M. F.; MORIYA, E. A. S.; IMAI, N. N.; ESTRABIS, N.; IANCZYK, F.; ARAÚJO, F. F.; LIESENBERG, V.; JORGE, L. A. de C.; LI, J.; MA, L.; GONÇALVES, W. N.; MARCATO JUNIOR, J.; CRESTE, J. E. A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements. Remote Sensing, n. 12, v. 6, a. 906, 2020. 1 - 21| Biblioteca(s): Embrapa Instrumentação. |
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| 12. | | 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. Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, v. 105, 102608, 2021. 1 - 10| Biblioteca(s): Embrapa Instrumentação. |
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| 13. | | 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.; LAUMANN, R. A.; FERREIRA, E. J.; OSCO, L. P.; RAMOS, A. P. M.; LI, J.; JORGE, L. A. de C. Prediction of insect-herbivory-damage and insect-type attack in maize plants using hyperspectral data. International Journal of Applied Earth Observation and Geoinformation, v. 105, 102608, 2021. 1 - 10| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| Registros recuperados : 13 | |
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 | 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
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Biblioteca(s): |
Embrapa Instrumentação; Embrapa Recursos Genéticos e Biotecnologia. |
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Data corrente: |
10/09/2021 |
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Data da última atualização: |
03/10/2023 |
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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: |
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. |
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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. |
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Título: |
Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Precision Agriculture, 2021. |
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DOI: |
https://doi.org/10.1007/s11119-021-09845-4 |
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Idioma: |
Inglês |
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Notas: |
Na publicação: Maria Carolina Blassioli-Moraes; Raúl Alberto Alaumann. |
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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 |
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Palavras-Chave: |
Insect damage; Machine learning; Spectral data; Theoretical model. |
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Categoria do assunto: |
-- |
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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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