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| Registros recuperados : 60 | |
| 4. |  | MAGALHÃES, D. M.; MICHEREFF, M. F. F.; HASSEMER, M. J.; BLASSIOLI-MORAES, M. C.; BORGES, M. Semioquímicos no controle de pragas. In: FONTES, E. M. G.; VALADARES-INGLIS, M. C. (Ed.). Controle biológico de pragas da agricultura. Brasília, DF : Embrapa, 2020. cap. 13, p. 415-450.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 6. | | MICHEREFF, M. F. F.; MORAES, M. C. B.; DINIZ, I. R.; LAUMANN, R. A.; BORGES, M. Interação tri-trófica soja - Euschistus heros (Hemiptera: Pentatomidae) – Telenomus podisi (Hymenoptera:Scelionidae). In: ENCONTRO DO TALENTO ESTUDANTIL DA EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA, 14., 2009, Brasília, DF. Anais: resumos dos trabalhos. Brasília, DF: Embrapa Recursos Genéticos e Biotecnologia, 2009. Resumo 080. p. 121| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 7. | | MICHEREFF, M. F. F.; MORAES, M. C. B.; DINIZ, I. R.; LAUMANN, R. A.; BORGES, M. Interação tri-trófica soja - Euschistus heros (Hemptera: Pentatomidae) - Telenomus podisi (Hymenoptera: Scelionidae). In: ENCONTRO BRASILEIRO DE ECOLOGIA QUÍMICA, 6, 2009, Viçosa, MG. Resumos... Viçosa, MG: Universidade Federal de Viçosa, 2009. p. 59.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 8. | | MICHEREFF, M. F. F.; LAUMANN, R. A.; BORGES, M.; DINIZ, I. R.; MORAES, M. C. B. Preferência de oviposição e biologia de Euschistus heros (Hemiptera: Pentatomidae) em diferentes cultivares de soja. In: ENCONTRO DO TALENTO ESTUDANTIL DA EMBRAPA RECURSOS GENÉTICOS E BIOTECNOLOGIA, 15., 2010, Brasília, DF. Anais: resumos dos trabalhos. Brasília, DF: Embrapa Recursos Genéticos e Biotecnologia, 2010. Resumo 085. p. 124| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 9. | | MORAES, M. C. B.; BIRKETT, M.; BORGES, M.; MICHEREFF, M. F. F.; LAUMANN, R. A. Biorational pest management in arable crops using semiochemicals and egg parasitoids. In: INTERNATIONAL CONGRESS OF ENTOMOLOGY - ICE, 26., 2022, Helsinki. Entomology for our planet: book of abstracts. [Kuopio: UEF, 2022]. Edited by Heikki M. T. Hokkanen, Ingeborg Menzler-Hokkanen. p. 100| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 10. | | MORAES, M. C. B.; MICHEREFF, M. F. F.; MAGALHÃES, D. M.; BORGES, M.; LAUMANN, R. A. Explorando semioquímicos de plantas para o manejo de insetos. In: SIMPÓSIO DE CONTROLE BIOLÓGICO, 13., 2013, Bonito, MS. Faça bonito: use controle biológico: anais. Brasília, DF: Embrapa, 2013.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 11. | | MICHEREFF, M. F. F.; VILELA, E. F.; MICHEREFF FILHO, M.; MAFRA NETO, A. Uso do feromônio sexual sintético para captura de machos da traça-das-cruciferas. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 35, n. 10, p. 1919-1926, out. 2000. Título em inglês: Synthetic sex pheromone use for field trapping of diamondback moth males.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 15. | | SILVA, F. A. C.; PANIZZI, A. R.; MORAES, M. C. B.; MICHEREFF, M. F. F.; LAUMANN, R. A.; BORGES, M. Fitoquímicos em vagens de soja BRS 267 com possível influência no comportamento de oviposição de-percevejos. In: ENCONTRO BRASILEIRO DE ECOLOGIA QUÍMICA, 5., 2007, Londrina. Resumos... Londrina: Embrapa Soja, 2007. 144 p. Organizado por Clara Beatriz Hoffmann-Campo, Odilon Ferreira Saraiva, Lenita Jacob Oliveira, Odilon Ferreira Saraiva, Simone Ery Grosskopf. p. 114.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 16. | | SILVA, F. A. C.; PANIZZI, A. R.; MORAES, M. C. B.; MICHEREFF, M. F. F.; LAUMANN, R. A.; BORGES, M. Fitoquímicos em vagens de soja BRS 267 com possível influência no comportamento de oviposição de-percevejos In: ENCONTRO BRASILEIRO DE ECOLOGIA QUÍMICA, 5., 2007, Londrina. Resumos... Londrina: Embrapa Soja, 2007. p. 114. Organizado por Clara Beatriz Hoffmann-Campo, Odilon Ferreira Saraiva, Lenita Jacob Oliveira, Odilon Ferreira Saraiva, Simone Ery Grosskopf.| Biblioteca(s): Embrapa Soja. |
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| 17. | | MICHEREFF, M. F. F.; BORGES, M.; LAUMANN, R. A.; DANIEL, D.; LAGO, C. L. do; BLASSIOLI-MORAES, M. C. The influence of resistant soybean cultivars on the biological development of Euschistus heros (Hemiptera: Pentatomidae). Journal of Plant Interactions, v.14, n. 1, p. 544-551, 2019.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 19. | | MICHEREFF FILHO, M.; SOBRAL, L. F.; FERREIRA, J. M. S.; RODRIGUES, A. R. dos S.; MICHEREFF, M. F. F. Adubação química, ataque do ácaro Aceria guerreronis e produtividade do coqueiro 'Anão-Verde'. Pesquisa Agropecuaria Brasileira, Brasília, DF, v. 43, n. 3, p. 303-308, mar. 2008.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia; Embrapa Tabuleiros Costeiros; Embrapa Unidades Centrais. |
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| 20. | | MICHEREFF, M. F. F.; RODRIGUES, A. R. dos S.; SILVA, J. da; MICHEREFF FILHO, M.; FERREIRA, J. M. dos S. Efeito do sistema de irrigação do coqueiro na intensidade de ataque do ácaro Aceria guerreronis (Acari:Eriophyidae). In: CONGRESSO BRASILEIRO DE ENTOMOLOGIA, 22., 2008, Uberlândia. Ciência, tecnologia e inovação: anais. Viçosa, MG: UFV, 2008. 1 CD-ROM. ResumoID:1222-1.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| Registros recuperados : 60 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Instrumentação. |
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Data corrente: |
24/05/2022 |
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Data da última atualização: |
23/01/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
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. |
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Afiliação: |
MIGUEL BORGES, Cenargen; MARIA CAROLINA BLASSIOLI MORAES, Cenargen; RAUL ALBERTO LAUMANN, Cenargen; LUCIO ANDRE DE CASTRO JORGE, CNPDIA. |
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Título: |
An impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Infrared Physics & Technology, v. 123, 104203, 2022. |
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Páginas: |
13 p. |
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ISSN: |
1350-4495 |
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DOI: |
10.1016/j.infrared.2022.104203 |
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Idioma: |
Inglês |
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Conteúdo: |
Spectroscopy is essential to understand a series of phenomena in multiple fields of study. In remote sensing, vegetation analysis is one of the most prominent fields to explore, aiming to improve a specific task. As a task, modeling insect damage in the plants is essential to establish the correct management of agricultural farmlands. Hyperspectral data, which can be acquired with field spectroscopy at plant or leaf level, is a non-direct, rapid, and trustworthy approach to indicate its health. However, the spectral redundancy inherent is a challenge for the information extraction process, making the pre-processing phase an essential part of the analysis. Currently, artificial intelligence techniques, mostly based on machine and deep learning methods, are a standard application in data processing, being pre-processing techniques an essential part of it. But few studies aimed to measure the impact of such processes in vegetation monitoring, specifically with insect damage and spectral data. Here, we provide an analysis of the impact of pre-processing techniques on machine learning algorithms’ performance over said classification task. For this, we used a field spectroradiometer that operates within the 350–1,000 nm and 1,000–2,500 nm ranges. The dataset was composed of multiple spectral measurements that took place on different days in a controlled environment with soybean plants. As pre-processing techniques, methods like baseline removal, smoothing, first and second-order derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and principal components analysis (PCA) were investigated. Several machine learning algorithms and one deep learning method were applied to model the datasets. The impact of the pre-processing techniques was measured within validation metrics relate to its accuracy. Our results indicated that the Extra-Tree (ExT) algorithm was better, mainly when first-order derivative data were extracted from the dataset (accuracy equal to 93.68%). A ranking approach indicated that the most contributive spectral region situates at the near-infrared, between 784 and 911 nm. Our investigation also demonstrates that a deep neural network (DNN) did not return a satisfactory result over raw reflectance data. However, when considering a combination of PCA over the 2nd derivative data, it achieved similar results to the ExT algorithm (accuracy of 91.95%). The implications of such, alongside the ranking approach, are discussed in this paper. We hope that the information presented here serves as a framework for future research when applying pre-processing techniques alongside the machine and deep learning methods over spectral data. MenosSpectroscopy is essential to understand a series of phenomena in multiple fields of study. In remote sensing, vegetation analysis is one of the most prominent fields to explore, aiming to improve a specific task. As a task, modeling insect damage in the plants is essential to establish the correct management of agricultural farmlands. Hyperspectral data, which can be acquired with field spectroscopy at plant or leaf level, is a non-direct, rapid, and trustworthy approach to indicate its health. However, the spectral redundancy inherent is a challenge for the information extraction process, making the pre-processing phase an essential part of the analysis. Currently, artificial intelligence techniques, mostly based on machine and deep learning methods, are a standard application in data processing, being pre-processing techniques an essential part of it. But few studies aimed to measure the impact of such processes in vegetation monitoring, specifically with insect damage and spectral data. Here, we provide an analysis of the impact of pre-processing techniques on machine learning algorithms’ performance over said classification task. For this, we used a field spectroradiometer that operates within the 350–1,000 nm and 1,000–2,500 nm ranges. The dataset was composed of multiple spectral measurements that took place on different days in a controlled environment with soybean plants. As pre-processing techniques, methods like baseline removal, smoothing, first and second-order d... Mostrar Tudo |
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Palavras-Chave: |
DNN; Field spectroscopy. |
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Categoria do assunto: |
-- |
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Marc: |
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100 1 $aOSCO, L. P.
245 $aAn impact analysis of pre-processing techniques in spectroscopy data to classify insect-damaged in soybean plants with machine and deep learning methods.
260 $c2022
300 $a13 p.
520 $aSpectroscopy is essential to understand a series of phenomena in multiple fields of study. In remote sensing, vegetation analysis is one of the most prominent fields to explore, aiming to improve a specific task. As a task, modeling insect damage in the plants is essential to establish the correct management of agricultural farmlands. Hyperspectral data, which can be acquired with field spectroscopy at plant or leaf level, is a non-direct, rapid, and trustworthy approach to indicate its health. However, the spectral redundancy inherent is a challenge for the information extraction process, making the pre-processing phase an essential part of the analysis. Currently, artificial intelligence techniques, mostly based on machine and deep learning methods, are a standard application in data processing, being pre-processing techniques an essential part of it. But few studies aimed to measure the impact of such processes in vegetation monitoring, specifically with insect damage and spectral data. Here, we provide an analysis of the impact of pre-processing techniques on machine learning algorithms’ performance over said classification task. For this, we used a field spectroradiometer that operates within the 350–1,000 nm and 1,000–2,500 nm ranges. The dataset was composed of multiple spectral measurements that took place on different days in a controlled environment with soybean plants. As pre-processing techniques, methods like baseline removal, smoothing, first and second-order derivatives, standard normal variate (SNV), multiplicative scatter correction (MSC), and principal components analysis (PCA) were investigated. Several machine learning algorithms and one deep learning method were applied to model the datasets. The impact of the pre-processing techniques was measured within validation metrics relate to its accuracy. Our results indicated that the Extra-Tree (ExT) algorithm was better, mainly when first-order derivative data were extracted from the dataset (accuracy equal to 93.68%). A ranking approach indicated that the most contributive spectral region situates at the near-infrared, between 784 and 911 nm. Our investigation also demonstrates that a deep neural network (DNN) did not return a satisfactory result over raw reflectance data. However, when considering a combination of PCA over the 2nd derivative data, it achieved similar results to the ExT algorithm (accuracy of 91.95%). The implications of such, alongside the ranking approach, are discussed in this paper. We hope that the information presented here serves as a framework for future research when applying pre-processing techniques alongside the machine and deep learning methods over spectral data.
653 $aDNN
653 $aField spectroscopy
700 1 $aFURUYA, D. E. G.
700 1 $aFURUYA, M. T. G.
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700 1 $aGONÇALVEZ, W. N.
700 1 $aMARCATO JUNIOR, J.
700 1 $aBORGES, M.
700 1 $aBLASSIOLI-MORAES, M. C.
700 1 $aMICHEREFF, M. F. F.
700 1 $aAQUUINO, M. F. S.
700 1 $aLAUMANN, R. A.
700 1 $aLISENBERG, V.
700 1 $aRAMOS, A. P. M.
700 1 $aJORGE, L. A. de C.
773 $tInfrared Physics & Technology$gv. 123, 104203, 2022.
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