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| Registros recuperados : 92 | |
| 1. |  | VIEIRA, J. I. G.; BRAGA, L. G.; CHU, T. C. S.; FERREIRA, P. H.; GUIMARÃES, S. E. F.; MARTINS, M. F.; PANETTO, J. C. do C.; MACHADO, M. A.; SILVA, D. B. dos S.; BONAFÉ, C. M.; MAGALHÃES, A. F. B.; SILVA, M. V. G. B.; VERARDO, L. L. Resequencing of Brazilian locally adapted cattle breeds revealed variants in candidate genes and transcription factors for meat fatty acid profile. Journal of Animal Breeding and Genetics, 2024. First online.| Biblioteca(s): Embrapa Gado de Leite. |
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| 2. | | BRAGA, L. P.; TANENTZAP, A. J.; LEE, B.; TSAI, S. M.; RAAIJMAKERS, J. M.; MENDES, R.; MENDES, L. W. Diversity of viruses and viroids in the rhizosphere of common bean cultivars differing in resistance to the fungal root pathogen Fusarium oxysporum. Applied Soil Ecology, v. 190, article 105018, 2023.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 3. | | BRAGA, L. G.; CHUD, T. C. S.; WATANABE, R. N.; SAVEGNAGO, R. P.; SENA, T. M.; CARMO, A. S. do; MACHADO, M. A.; PANETTO, J. C. do C.; SILVA, M. V. G. B.; MUNARI, D. P. Identification of copy number variations in the genome of Dairy Gir cattle. PLoS ONE, v. 18, n. 4, e0284085, 2023.| Biblioteca(s): Embrapa Gado de Leite. |
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| 4. | | BRAGA, L. I. G.; PINHEIRO, R. R.; SILVA, M. V. G. B.; FACO, O.; ANDRIOLI, A.; SIDER, L. H. Caracterização fenotípica de animais de propriedades com incidência de artrite encefalite caprina para a condução de um estudo de associação genômica ampla. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA CAPRINOS E OVINOS, 11., 2022, Sobral. Anais... Sobral: Embrapa Caprinos e Ovinos, 2023. p. 18-19. (Embrapa Caprinos e Ovinos. Eventos técnicos & científicos).| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 5. | | BRAGA, L. I. G.; ANDRIOLI, A.; PINHEIRO, R. R.; SILVA, M. V. G. B.; FACO, O.; SIDER, L. H. Caracterização fenotípica de animais de propriedades com incidência de artrite encefalite caprina para a condução de um estudo de associação genômica ampla. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA CAPRINOS E OVINOS, 10., 2021, Sobral. Anais... Sobral: Embrapa Caprinos e Ovinos, 2022. p. 23-24.| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 6. | | BRAGA, L. I. G.; HONÓRIO, F. L. de L. H.; BRANDÃO, I. S.; LIMA, A. M. C.; PEIXOTO, R. M.; PINHEIRO, R. R. Análise carpo-metacarpianas em rebanhos leiteiros intensivo no Brasil com artrite encefalite caprina. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA CAPRINOS E OVINOS, 9., 2020, Sobral. Anais... Sobral: Embrapa Caprinos e Ovinos, 2021. p. 36-37. Embrapa Caprinos e Ovinos. Documentos, 140).| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 7. | | SILVA, G. G. DA; BRAGA, L. E. O.; OLIVEIRA, E. C. S. DE; TINTI, S. V.; CARVALHO, J. E. de; LAZARINI, J. G.; ROSALEN, P. L.; DIONISIO, A. P.; RUIZ, A. L. T. G. Cashew apple byproduct: Gastroprotective effects of standardized extract. Journal of Ethnopharmacology, v. 269, 113744, 6 April 2021.| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| 8. | | BRANDÃO, S. I.; SOUSA, A. L. M.; SOUZA, S. C. R.; AMARAL, G. P.; BRAGA, L. I. G.; PINHEIRO, R. R. Atividade antiviral em leite do extrato de Melia azedarach contra o vírus da artrite encefalite caprina. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA CAPRINOS E OVINOS, 9., 2020, Sobral. Anais... Sobral: Embrapa Caprinos e Ovinos, 2021. p. 38-39. Embrapa Caprinos e Ovinos. Documentos, 140).| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 9. | | HONÓRIO, F. C. de L.; BRAGA, L. I. G.; LIMA, A. M. C.; AZEVEDO, D. A. A. de; PEIXOTO, R. M.; PINHEIRO, R. R. Avaliação clínica de escore corporal em rebanhos leiteiros intensivos no Brasil com artrite encefalite caprina. In: ENCONTRO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA CAPRINOS E OVINOS, 9., 2020, Sobral. Anais... Sobral: Embrapa Caprinos e Ovinos, 2021. p. 40-41. Embrapa Caprinos e Ovinos. Documentos, 140).| Biblioteca(s): Embrapa Caprinos e Ovinos. |
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| 11. | | FERNANDES, G. W.; ARANTES-GARCIA, L.; BARBOS, M.; BARBOS, N. P. U.; BATISTA, E. K. L.; BEIROZ, W.; RESENDE, F. M.; ABRAHÃO, A.; ALMADA, E. D.; ALVES, E.; ALVES, N. J.; ANGRISANO, P.; ARISTA, M.; ARROYO, J.; ARRUDA, A. J.; BAHIA, T. de O.; BRAGA, L.; BRITO, L.; CALLISTO, M.; CAMINHA-PAIVA, D.; CARVALHO, M.; CONCEIÇÃO, A. A.; COSTA, L. N.; CRUZ, A.; CUNHA-BLUM, J.; DAGEVOS, J.; DIAS, B. F. S.; PINTO, V. D.; DIRZO, R.; DOMINGOS, D. Q.; ECHTERNACHT, L.; FERNANDES, S.; FIGUEIRA, J. E. C.; FIORINI, C. F.; GIULIETTI, A. M.; GOMES, A.; GOMES, V. M.; GONTIJO, B.; GOULART, F.; GUERRA, T. J.; JUNQUEIRA, P. A.; LIMA-SANTOS, D.; MARQUES, J.; MEIRA-NETO, J.; MIOLA, D. T. B.; MORELLATO, L. P. C.; NEGREIROS, D.; NEIRE, E.; NEVES, A. C.; NEVES, F. S.; NOVAIS, S.; OKI, Y.; OLIVEIRA, E.; OLIVEIRA, R. S.; PIVARI, M. O.; PONTES JUNIOR, E.; RANIERI, B. D.; RIBAS, R. P.; SCARIOT, A.; ECHAEFER, C. E.; SENA, L.; SILVA, P. G. da; SIQUEIRA, P. R.; SOARES, N. C.; SOARES-FILHO, B.; SOLAR, R.; TABARELLI, M.; VASCONCELLOS, R.; VILELA, E.; SILVEIRA, F. A. O. Biodiversity and ecosystem services in the Campo Rupestre: a road map for the sustainability of the hottest Brazilian biodiversity hotspot. Perspectives in Ecology and Conservation, v. 18, p. 213-222, 2020| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 13. | | SILVA, L. de J. de S.; MONTEIRO, M. de A.; BRAGA, L. R. F.; MIRANDA, T. N. de O. Uma perspectiva decolonial na abordagem da construção da resistência e mobilização das comunidades de Juruti Velho em face do advento da Alcoa em seu território, estado do Pará, Amazônia, Brasil. Nova Revista Amazônica, v. 8, n. 3, p. 167-187, dez. 2020.| Biblioteca(s): Embrapa Amazônia Ocidental. |
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| 14. | | SILVA, G. G. DA; TORRE, A. D.; BRAGA, L. E. DE O.; BACHIEGA, P.; TINTI, S. V.; CARVALHO, J. E. DE; DIONISIO, A. P.; RUIZ, A. L. T. G. Yellow-colored extract from cashew by product: nonclinical safety assessment. Regulatory Toxicology and Pharmacology, [New York], v. 115, artigo 104699, 7 p., Aug. 2020.| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| 15. | | BICUDO, A. J. A.; ARAUJO, T. A. T.; BRAGA, L. G. T.; TONINI, W. C. T.; HISANO, H. Apparent digestibility of conventional and alternative feedstuffs by hybrid tambacu juveniles. Anais da Academia Brasileira de Ciências, Rio de Janeiro, v. 90, n. 1, p. 471-478, 2018.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 16. | | BRAGA, L. F.; OLIVEIRA, F. A. de; COUTO, E. A. P. do; SANTOS, K. F. E. N.; FERREIRA, E. P. de B.; MARTIN-DIDONET, C. C. G. Polyphasic characterization of bacteria obtainedfrom upland rice cultivated in Cerrado soil. Brazilian Journal of Microbiology, v. 49, n. 1, p. 20-28, 2018.| Biblioteca(s): Embrapa Arroz e Feijão. |
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| 17. | | ALVES FILHO, E. G.; BRAGA, L. N.; SILVA, L. M. A. e; MIRANDA, F. R. de; SILVA, E. de O.; CANUTO, K. M.; MIRANDA, M. R.; BRITO, E. S. de; ZOCOLO, G. J. Physiological changes for drought resistance in diferent species of Phyllanthus. Scientific Reports, v. 8, art. 15141, 2018.| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| 19. | | BRAGA, L. A. C.; RODRIGUES, M. J.; SOUZA FILHO, M. de S. M. de; AZEREDO, H. M. C. de; SILVA, E. de O.; MUNIZ, C. R.; OLIVEIRA, A. V. de; RIBEIRO, H. L. Filmes comestíveis de emulsões de polissacarídeos de algas com cera de carnaúba. Fortaleza: Embrapa Agroindústria Tropical, 2017. 21 p. (Embrapa Agroindústria Tropical. Boletim de Pesquisa e Desenvolvimento, 134).| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| Registros recuperados : 92 | |
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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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