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Registro Completo |
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Biblioteca(s): |
Embrapa Hortaliças. |
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Data corrente: |
08/11/2021 |
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Data da última atualização: |
08/11/2021 |
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Tipo da produção científica: |
Boletim de Pesquisa e Desenvolvimento |
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Autoria: |
MICHEREFF FILHO, M.; FONSECA, M. E. N.; BOITEUX, L. S.; SOUSA, N. C. de M.; SILVA, K. F. A. de S.; SILVA, P. A. da; SILVA, P. S. da; MOITA, A. W.; TORRES, J. B. |
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Afiliação: |
MIGUEL MICHEREFF FILHO, CNPH; MARIA ESTHER DE N FONSECA BOITEUX, CNPH; LEONARDO SILVA BOITEUX, CNPH; NAYARA CRISTINA DE MAGALHÃES SOUSA, Universidade Federal Rural de Pernambuco.; KARLA FERNANDA AYRES DE SOUZA SILVA, Universidade Federal Rural de Pernambuco.; PALOMA ALVES DA SILVA; PATRÍCIA SANTOS DA SILVA, Bolsista FAPDF/ CNPH; ANTONIO WILLIAMS MOITA, CNPH; JORGE BRAZ TORRES, Universidade Federal Rural de Pernambuco. |
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Título: |
Suscetibilidade de cultivares de tomateiro à Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae). |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Brasília, DF: Embrapa Hortaliças, 2021. |
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Páginas: |
31 p. |
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Série: |
(Embrapa Hortaliças. Boletim de pesquisa e desenvolvimento, 232). |
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ISSN: |
1677-2229 |
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Idioma: |
Português |
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Conteúdo: |
Este trabalho avaliou a suscetibilidade de nove cultivares de tomateiro à infestação de H. armigera em casa de vegetação, com chance de escolha e determinou o desempenho da fase larval da praga nesses genótipos, em condições de laboratório. As cultivares BRS Zamir e BRS Sena apresentaram as menores infestações de lagartas, enquanto BRS Zamir apresentou a menor porcentagem de frutos danificados (11,42%) e o menor consumo de polpa (8,14 g/lagarta), respectivamente. A menor duração do período larval ocorreu em insetos alimentados com frutos da cultivar AP533 (10,94 dias), enquanto os maiores tempos de desenvolvimento foram constatados com frutos de BRS Zamir e BRS Sena (18,3 a 18,6 dias). O peso dos insetos no final da fase larval não diferiu entre os genótipos (0,43 a 0,52 g/lagarta) e os níveis de mortalidade foram inferiores a 19%. Conclui-se que, apesar de efeitos adversos apresentados por alguns genótipos de tomateiro, H. armigera pode utilizar estratégias de compensação para consumo e uso de alimento que permitam alto desempenho da fase larval. |
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Thesagro: |
Praga de Planta; Tomate. |
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Thesaurus Nal: |
Solanum lycopersicum. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/227471/1/BPD-232-8nov2021.pdf
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Marc: |
LEADER 01942nam a2200277 a 4500
001 2135869
005 2021-11-08
008 2021 bl uuuu u0uu1 u #d
022 $a1677-2229
100 1 $aMICHEREFF FILHO, M.
245 $aSuscetibilidade de cultivares de tomateiro à Helicoverpa armigera (Hübner) (Lepidoptera$bNoctuidae).$h[electronic resource]
260 $aBrasília, DF: Embrapa Hortaliças$c2021
300 $a31 p.
490 $a(Embrapa Hortaliças. Boletim de pesquisa e desenvolvimento, 232).
520 $aEste trabalho avaliou a suscetibilidade de nove cultivares de tomateiro à infestação de H. armigera em casa de vegetação, com chance de escolha e determinou o desempenho da fase larval da praga nesses genótipos, em condições de laboratório. As cultivares BRS Zamir e BRS Sena apresentaram as menores infestações de lagartas, enquanto BRS Zamir apresentou a menor porcentagem de frutos danificados (11,42%) e o menor consumo de polpa (8,14 g/lagarta), respectivamente. A menor duração do período larval ocorreu em insetos alimentados com frutos da cultivar AP533 (10,94 dias), enquanto os maiores tempos de desenvolvimento foram constatados com frutos de BRS Zamir e BRS Sena (18,3 a 18,6 dias). O peso dos insetos no final da fase larval não diferiu entre os genótipos (0,43 a 0,52 g/lagarta) e os níveis de mortalidade foram inferiores a 19%. Conclui-se que, apesar de efeitos adversos apresentados por alguns genótipos de tomateiro, H. armigera pode utilizar estratégias de compensação para consumo e uso de alimento que permitam alto desempenho da fase larval.
650 $aSolanum lycopersicum
650 $aPraga de Planta
650 $aTomate
700 1 $aFONSECA, M. E. N.
700 1 $aBOITEUX, L. S.
700 1 $aSOUSA, N. C. de M.
700 1 $aSILVA, K. F. A. de S.
700 1 $aSILVA, P. A. da
700 1 $aSILVA, P. S. da
700 1 $aMOITA, A. W.
700 1 $aTORRES, J. B.
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Registro original: |
Embrapa Hortaliças (CNPH) |
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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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