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| Registros recuperados : 336 | |
| 321. |  | MAGALHÃES, D. M.; BORGES, M.; LAUMANN, R. A.; SUJII, E. R.; MAYON, P.; CAULFIELD, J. C; MIDEGA, C. A. O.; KHAN, Z. R.; PICKETT, P. J. A.; BIRKETT, M. A.; MORAES, M. C. B. Semiochemicals from herbivory induced cotton plants enhance the foraging behavior of the cotton boll weevil, Anthonomus grandis. Journal of Chemical Ecology, v. 38, p. 1528-1538, 2012.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 322. | | MICHEREFF, M. F. F.; GRYNBERG, P.; TOGAWA, R. C.; COSTA, M. M. do C.; LAUMANN, R. A.; ZHOU, J.-J.; SCHIMMELPFENG, P. H. C.; BORGES, M.; PICKETT, J. A.; BIRKETT, M. A.; MORAES, M. C. B. Priming of indirect defence responses in maize is shown to be genotype-specific. Arthropod-Plant Interactions, v. 15, p. 313-328, 2021. Na publicação: Marcos M. C. Costa; Maria Carolina Blassioli-Moraes.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 323. | | FARIAS, L. R.; SCHIMMELPFENG, P. H. C.; TOGAWA, R. C.; COSTA, M. M. do C.; GRYNBERG, P.; MARTINS, N. F.; BORGES, M.; MORAES, M. C. B.; LAUMANN, R. A.; BÁO, S. N.; PAULA, D. P. Transcriptome-based identification of highly similar odorant-binding proteins among neotropical stink bugs and their egg parasitoid. Plos One, jul., 2015.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 324. | | HASSEMER, M. J.; BLASSIOLI-MORAES, M. C.; BORGES, M.; LAUMANN, R. A.; BEMQUERER, M.; RODRIGUES, M.; COSTA, E. S.; VAZ JUNIOR, S.; MAGALHÃES, D. M.; MICHEREFF, M. F. F. O uso de nanopartículas para a liberação controlada de semioquímicos de insetos e plantas. In: WORKSHOP DE NANOTECNOLOGIA APLICADA AO AGRONEGÓCIO, 9., 2017, São Carlos, SP. Anais ... São Carlos: Embrapa Instrumentação, 2017. p. 466-470. Editores: Caue Ribeiro; Elaine Cristina Paris; Luiz Henrique Capparelli Mattoso; Marcelo Porto Bemquerer; Maria Alice Martins; Odílio Benedito Garrido De Assis.| Biblioteca(s): Embrapa Agroenergia; Embrapa Recursos Genéticos e Biotecnologia. |
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| 326. | | VANÍCKOVÁ, L.; HERNÁNDEZ-ORTIZ, V.; BRAVO, I. S. J.; DIAS, V.; RORIZ, A. K. P.; LAUMANN, R. A.; MENDONÇA, A. de L.; PARANHOS, B. A. J.; NASCIMENTO, R. R. do. Current knowledge of the species complex Anastrepha fraterculus (Diptera, Tephritidae) in Brazil. ZooKeys, v. 540, p. 211-237, 2015.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 327. | | DIAS, V. S.; SILVA, J. G.; LIMA, K. M.; PETITINGA, C. S. C. D.; HERNÁNDEZ-ORTIZ, V.; LAUMANN, R. A.; PARANHOS, B. A. J.; URAMOTO, K.; ZUCCHI, R. A.; JOACHIM-BRAVO, I. S. An integrative multidisciplinary approach to understanding cryptic divergence in Brazilian species of the Anastrepha fraterculus complex (Diptera: Tephritidae). Biological Journal of the Linnean Society, v. 117, p. 725-746, 2016.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 328. | | MORAES, M. C. B.; VENZON, M.; SILVEIRA, L. C. P.; GONTIJO, L. M.; TOGNI, P. H. B.; SUJII, E. R.; HARO, M. M.; BORGES, M.; MICHEREFF, M. F. F.; AQUINO, M. F. S. de; LAUMANN, R. A.; CAULFIELD, J.; BIRKETT, M. Companion and smart plants: scientific background to promote conservation biological control. Neotropical Entomology, v. 51, p. 171-187, 2022. Na publicação: Maria Carolina Blassioli-Moraes.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 329. | | SOUZA, L. M. de; ROSSI, M. B.; TOREZANI, K. R. de S.; SOUZA, A. E. R. de A.; HARTERREITEN-SOUZA, E. S.; FRIZZO, T. L. M.; AQUINO, M. F. S. de; TOGNI, P. H. B.; SCHMIDT, F. G. V.; LAUMANN, R. A.; SOUSA, A. A. T. C. de; FONTES, E. M. G.; PIRES, C. S. S.; SUJII, E. R. Coleção entomológica de trabalho do Laboratório de Ecologia e Biossegurança da Embrapa Recursos Genéticos e Biotecnologia. Brasília, DF: Embrapa Recursos Genéticos e Biotecnologia, 2015. 28 p. (Embrapa Recursos Genéticos e Biotecnologia. Documentos, 349).| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 330. | | 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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| 331. | | CONTI, E.; AVILA, G.; BARRAT, B.; CINGOLANI, F.; COLAZZA, S.; GUARINO, S.; HOELMER, K.; LAUMANN, R. A.; MAISTRELLO, L.; MARTEL, G.; PERI, E.; RODRIGUEZ-SAONA, C.; RONDONI, G.; ROSTÁS, M.; ROVERSI, P. F.; FORZA, R. F. H.; TAVELLA, L.; WAJNBERG, E. Biological control of invasive stink bugs: review of global state and future prospects. Entomologia Experimentalis et Applicata, 2020. 6th International Entomophagous Insects Conference, Perugia, Italy Sept. 2019.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 332. | | 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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| 333. | | 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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| 334. | | 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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| 335. | | MICHEREFF, M. M.; MAGALHÃES, D. M.; HASSEMER, M. J.; LAUMANN, R. A.; ZHOU, J.-J.; RIBEIRO, P. E. de A.; VIANA, P. A.; GUIMARAES, P. E. de O.; SCHIMMELPFENG, P. H. C.; BORGES, M.; PICKETT, J. A.; BIRKETT, M. A.; BLASSIOLI-MORAES, M. C. Variability in herbivore-induced defence signalling across different maize genotypes impacts significantly on natural enemy foraging behaviour. Journal of Pest Science, v. 92, p. 723-736, 2019.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 336. | | MICHEREFF, M. M.; MAGALHÃES, D. M.; HASSEMER, M. J.; LAUMANN, R. A.; ZHOU, J.-J.; RIBEIRO, P. E. de A.; VIANA, P. A.; GUIMARAES, P. E. de O.; SCHIMMELPFENG, P. H. C.; BORGES, M.; PICKETT, J. A.; BIRKETT, M. A.; MORAES, M. C. B. Variability in herbivore-induced defence signalling across different maize genotypes impacts significantly on natural enemy foraging behaviour. Journal of Pest Science, v. 92, p. 723-736, 2019.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| Registros recuperados : 336 | |
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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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