| |
|
|
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Meio-Norte. |
|
Data corrente: |
16/02/2009 |
|
Data da última atualização: |
27/06/2022 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
SAGRILO, E.; VIDIGAL FILHO, P. S.; PEQUENO, M. G.; VIDIGAL, M. C. G.; KVITSCHAL, M. V. |
|
Afiliação: |
Edvaldo Sagrilo, Embrapa Meio-Norte; Pedro Soares Vidigal Filho, Universidade Estadual de Maringá; Manoel Genildo Pequeno, Universidade Estadual de Maringá; Maria Celeste Gonçalves-Vidigal, Universidade Estadual de Maringá; Marcus Vinícius Kvitschal, Universidade Estadual de Maringá. |
|
Título: |
Dry matter production and distribution in three cassava (Manihot esculenta Crantz) cultivars during the second vegetative plant cycle. |
|
Ano de publicação: |
2008 |
|
Fonte/Imprenta: |
Brazilian Archives of Biology and Technology, Curitiba, v. 51, n. 6, p. 1079-1087, nov./dec. 2008. |
|
ISSN: |
1516-8913 |
|
Idioma: |
Inglês |
|
Conteúdo: |
A study was carried out in Araruna County, State of Paraná, to understand the relationship between the total dry matter yield and its proportion allocated to the storage roots of cassava (Manihot esculenta Crantz) plants in the second vegetative cycle. The experimental design was a randomized complete block in split-plot scheme with four replications. The plots consisted of the Mico, IAC 13 and IAC 14 cultivars and the monthly harvesting dates were assessed in the sub-plots. The results showed that the Mico and IAC 13 cultivars were more efficient in allocating dry matter to the storage roots. The IAC 14 cultivar allocated a higher proportion of assimilates to stems compared with the other two cultivars. With regard to the influence of harvesting time, the lowest harvest indexes were observed in the periods of more intense vegetative growth. However, the highest carbohydrate proportions were allocated to the storage roots during periods of low vegetative growth. |
|
Palavras-Chave: |
Dry matter allocation; Harvesting time. |
|
Thesaurus Nal: |
harvest index. |
|
Categoria do assunto: |
-- |
|
URL: |
https://www.scielo.br/pdf/babt/v51n6/01.pdf
|
|
Marc: |
LEADER 01692naa a2200217 a 4500
001 1070513
005 2022-06-27
008 2008 bl uuuu u00u1 u #d
022 $a1516-8913
100 1 $aSAGRILO, E.
245 $aDry matter production and distribution in three cassava (Manihot esculenta Crantz) cultivars during the second vegetative plant cycle.
260 $c2008
520 $aA study was carried out in Araruna County, State of Paraná, to understand the relationship between the total dry matter yield and its proportion allocated to the storage roots of cassava (Manihot esculenta Crantz) plants in the second vegetative cycle. The experimental design was a randomized complete block in split-plot scheme with four replications. The plots consisted of the Mico, IAC 13 and IAC 14 cultivars and the monthly harvesting dates were assessed in the sub-plots. The results showed that the Mico and IAC 13 cultivars were more efficient in allocating dry matter to the storage roots. The IAC 14 cultivar allocated a higher proportion of assimilates to stems compared with the other two cultivars. With regard to the influence of harvesting time, the lowest harvest indexes were observed in the periods of more intense vegetative growth. However, the highest carbohydrate proportions were allocated to the storage roots during periods of low vegetative growth.
650 $aharvest index
653 $aDry matter allocation
653 $aHarvesting time
700 1 $aVIDIGAL FILHO, P. S.
700 1 $aPEQUENO, M. G.
700 1 $aVIDIGAL, M. C. G.
700 1 $aKVITSCHAL, M. V.
773 $tBrazilian Archives of Biology and Technology, Curitiba$gv. 51, n. 6, p. 1079-1087, nov./dec. 2008.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Meio-Norte (CPAMN) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
 | 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
|
Biblioteca(s): |
Embrapa Instrumentação; Embrapa Recursos Genéticos e Biotecnologia. |
|
Data corrente: |
10/09/2021 |
|
Data da última atualização: |
03/10/2023 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 1 |
|
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. |
|
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. |
|
Título: |
Detecting the attack of the fall armyworm (Spodoptera frugiperda) in cotton plants with machine learning and spectral measurements. |
|
Ano de publicação: |
2021 |
|
Fonte/Imprenta: |
Precision Agriculture, 2021. |
|
DOI: |
https://doi.org/10.1007/s11119-021-09845-4 |
|
Idioma: |
Inglês |
|
Notas: |
Na publicação: Maria Carolina Blassioli-Moraes; Raúl Alberto Alaumann. |
|
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 |
|
Palavras-Chave: |
Insect damage; Machine learning; Spectral data; Theoretical model. |
|
Categoria do assunto: |
-- |
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Instrumentação (CNPDIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
