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| Registros recuperados : 11 | |
| 1. |  | MATEUS, G. P.; CRUSCIOL, C. A. C.; PARIZ, C. M.; COSTA, N. R.; BORGHI, E.; COSTA, C.; MARTELLO, J. M.; CASTILHOS, A. M.; FRANZLUEBBERS, A. J.; CANTARELLA, H. Corn intercropped with tropical perennial grasses as affected by sidedress nitrogen application rates. Nutrient Cycling in Agroecosystems, 2019. Publicado online em 30 nov. 2019.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 2. | | MATEUS, G. P.; CRUSCIOL, C. A. C.; PARIZ, C. M.; BORGHI, E.; COSTA, C.; MARTELLO, J. M.; FRANZLUEBBERS, A. J.; CASTILHOS, A. M. Sidedress nitrogen application rates to sorghum intercropped with tropical perennial grasses. Agronomy Journal, Madison, v. 108, n. 1, p. 433-447, 2016.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 3. | | PARIZ, C. M.; COSTA, C.; CRUSCIO, C. A. C.; MEIRELLES, P. R. de L.; CASTILHOS, A. M. de; ANDREOTTI, M.; COSTA, N. R.; MARTELLO, J. M. Silage production of corn intercropped with tropical forages in an integrated crop-livestock system with lambs. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 52, n. 1, p. 54-62, jan. 2017. Título em português: Produção de silagem de milho consorciado com forrageiras tropicais em sistema de integração lavoura-pecuária com cordeiros.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 4. | | RIBEIRO, F. G.; JORGE, A. M.; FRANCISCO, C. de L.; CASTILHOS, A. M. de; PARIZ, C. M.; SILVA, M. B. da. Simbióticos e monensina sódica no desempenho e na qualidade da carne de novilhas mestiças Angus confinadas. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 50, n. 10, p. 958-966, out. 2015. Título em inglês: Synbiotics and sodic monensin on performance and meat quality of Angus crossbred heifers on feedlot.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 5. | | COSTA, N. R.; CRUSCIO, C. A. C.; LIMA, C. G. da R.; CASTILHOS, A. M. de; SOUZA, D. M. de; BONINI, C. dos S. B.; PARIZ, C. M. Yield and nutritive value of the silage of corn intercropped with tropical perennial grasses. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 52, n. 1, p. 63-73, jan. 2017. Título em português: Produtividade e valor nutritivo da silagem de milho em consórcio com gramíneas perenes tropicais.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 6. | | CRUSCIOL, C. A. C.; MARQUES, R. R.; CARMEIS FILHO, A. C. A.; SORATTO, R. P.; COSTA, C. H. M.; FERRARI NETO, J.; CASTRO, G. S. A.; PARIZ, C. M.; CASTILHOS, A. M.; FRANZLUEBBERS, A. J. Lime and gypsum combination improves crop and forage yields and estimated meat production and revenue in a variable charge tropical soil. Nutrient Cycling in Agroecosystems, v. 115, n. 312, p. 1-26, 2019.| Biblioteca(s): Embrapa Territorial. |
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| 7. | | CRUSCIOL, C. A. C.; MARQUES, R. A.; CARMEIS FILHO, A. C. A.; SORATTO, R. P.; COSTA, C. H. M.; FERRARI NETO, F.; CASTRO, G. S. A.; PARIZ, C. M.; CASTILHOS, A. M DE. Annual crop rotation of tropical pastures with no-till soil as affectedby lime surface application. European Journal of Agronomy, v. 80, p. 88?104, oct. 2016.| Biblioteca(s): Embrapa Territorial. |
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| 8. | | OLIVEIRA, P. de; GUERREIRO, M. F.; NICODEMO, M. L. F.; VINHOLIS, M. de M. B.; MOLTOCARO, R. C. R.; MEIRELLES, P. R. de L.; PARIZ, C. M.; RODRIGUES, G. S.; CASTILHOS, A. M. DE; ROMEIRO, J. C. T. Establishment of diversified crop-livestock-forestry systems by arboreal forage association. In: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS, 2., 2021. Proceedings reference... Brasília, DF: Embrapa, 2021. p. 461-466.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 9. | | OLIVEIRA, P. de; GUERREIRO, M. F.; NICODEMO, M. L. F.; VINHOLIS, M. de M. B.; MOLTOCARO, R. C. R.; MEIRELLES, P. R. de L.; PARIZ, C. M.; RODRIGUES, G. S.; CASTILHOS, A. M. DE; ROMEIRO, J. C. T. Establishment of diversified crop-livestock-forestry systems by arboreal forage association. In: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS: 100% DIGITAL, 2., 2021. WCCLF 2021 proceedings. Campo Grande, MS: Embrapa Gado de Corte, 2021. p.461-466.| Biblioteca(s): Embrapa Meio Ambiente. |
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| 10. | | BARROS, J. DA S.; GOMES, V. C.; MEIRELLES, P. R. DE L.; MATTA, F. de P.; PARIZ, C. M.; CASTILHOS, A. M. DE; GOMES, T. G. DE J.; COSTA, C. Correlation of shear strength and physicochemical properties as a criterion for the selection of accessions of Paspalum grasses. Acta Scientiarum-Agronomy, v. 45, e56280, 2022. 9 p.| Biblioteca(s): Embrapa Pecuária Sudeste. |
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| 11. | | COSTA, J. A. A. da; PARIZ, C. M.; FROTA, M. N. L. da; REIS, F. A.; COSTA, C.; ARAUJO NETO, R. B. de; TEIXEIRA NETO, M. L.; MEIRELLES, P. R. de L.; FEIJO, G. L. D.; CASTILHOS, A. M. de; CATTO, J. B.; CRUSCIOL, C. A. C.; CARVALHO, G. M. C. Produção de ovinos de corte em sistemas integrados. In: BUNGENSTAB, D. J.; ALMEIDA, R. G. de; LAURA, V. A.; BALBINO, L. C.; FERREIRA, A. D. (Ed.). ILPF: inovação com integração de lavoura, pecuária e floresta. Brasília, DF: Embrapa, 2019. p. 241-261| Biblioteca(s): Embrapa Caprinos e Ovinos; Embrapa Gado de Corte; Embrapa Meio-Norte. |
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| Registros recuperados : 11 | |
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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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