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Registro Completo |
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Biblioteca(s): |
Embrapa Trigo. |
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Data corrente: |
04/01/2018 |
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Data da última atualização: |
04/01/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SCHEUER, P. M.; MATTIONI, B.; LIMBERGER-BAYER, V. M.; TATSCH, P. O.; MIRANDA, M. Z. de; FRANCISCO, A. de. |
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Afiliação: |
PATRÍCIA M. SCHEUER, UFSC; BRUNA MATTIONI, UFSC; VALÉRIA M. LIMBERGER-BAYER, UFSM; PIHETRA OLIVEIRA TATSCH, CNPT; MARTHA ZAVARIZ DE MIRANDA, CNPT; ALICIA DE FRANCISCO, UFSC. |
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Título: |
Evaluation of whole-wheat flour blends with fat replacer. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Revista de Ciencia y Tecnología, Posadas, v. 19, n. 28, p. 4-10, 2017. |
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ISSN: |
1851-7587 |
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Idioma: |
Inglês |
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Thesagro: |
Farinha de trigo; Nutrição humana. |
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Thesaurus Nal: |
Human nutrition; Wheat flour. |
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Categoria do assunto: |
Q Alimentos e Nutrição Humana |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/170356/1/ID44269-2017v19n28p4RECyT.pdf
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Marc: |
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001 2084286
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008 2017 bl uuuu u00u1 u #d
022 $a1851-7587
100 1 $aSCHEUER, P. M.
245 $aEvaluation of whole-wheat flour blends with fat replacer.$h[electronic resource]
260 $c2017
650 $aHuman nutrition
650 $aWheat flour
650 $aFarinha de trigo
650 $aNutrição humana
700 1 $aMATTIONI, B.
700 1 $aLIMBERGER-BAYER, V. M.
700 1 $aTATSCH, P. O.
700 1 $aMIRANDA, M. Z. de
700 1 $aFRANCISCO, A. de
773 $tRevista de Ciencia y Tecnología, Posadas$gv. 19, n. 28, p. 4-10, 2017.
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Embrapa Trigo (CNPT) |
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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: |
LEADER 03727naa a2200337 a 4500
001 2143404
005 2024-01-23
008 2022 bl uuuu u00u1 u #d
022 $a1350-4495
024 7 $a10.1016/j.infrared.2022.104203$2DOI
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.
700 1 $aCORRÊA, D. V.
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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