|
|
| Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo |
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
18/12/2019 |
Data da última atualização: |
17/02/2021 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
SOUZA, M. F. de; AMARAL, L. R. do; OLIVEIRA, S. R. de M.; COUTINHO, M. A. N.; FERREIRA NETTO, C. |
Afiliação: |
MICAEL FELIPE DE SOUZA, Feagri/Unicamp; LUCAS RIOS DO AMARAL, Feagri/Unicamp; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; MARCOS ANTONIO NERIS COUTINHO, Feagri/Unicamp; CAMILA FERREIRA NETTO, Feagri/Unicamp. |
Título: |
Spectral differentiation of sugarcane from weeds. |
Ano de publicação: |
2020 |
Fonte/Imprenta: |
Biosystems Engineering, v. 190, p. 41-46, 2020. |
DOI: |
https://doi.org/10.1016/j.biosystemseng.2019.11.023 |
Idioma: |
Inglês |
Conteúdo: |
Site-specific application of herbicides is highly desirable for optimising its usage and reducing environmental damages. Thus, developing techniques for identification and mapping of weeds is necessary for a proper precision agriculture adoption. Such weed identification for site-specific management is difficult when the main crop is already established in the field. This study shows the possibility of differentiating sugarcane plants from weeds by the spectral behaviour of the leaves. The performance of two modelling methods, SIMCA (soft independent modelling by class analogy) and the RF algorithm (random forest) was compared. The simplification of the Vis-NIR spectrum into only four bands of interest (500e550 nm; 650e750 nm; 1300e1450 nm; and 1800e1900 nm) was verified by demonstrating they had the same differentiation ability as the full visible-near infra-red spectrum. Thus, it was shown that performing the proper band selection and local calibration using a spectral classification approach may allow weed mapping and facilitate localised herbicide application. |
Palavras-Chave: |
Classification models; Post-emergent herbicides; Random forest; Site-specific management. |
Thesagro: |
Agricultura de Precisão; Cana de Açúcar; Herbicida. |
Thesaurus Nal: |
Herbicides; Precision agriculture; Sugarcane. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01980naa a2200301 a 4500 001 2117231 005 2021-02-17 008 2020 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1016/j.biosystemseng.2019.11.023$2DOI 100 1 $aSOUZA, M. F. de 245 $aSpectral differentiation of sugarcane from weeds.$h[electronic resource] 260 $c2020 520 $aSite-specific application of herbicides is highly desirable for optimising its usage and reducing environmental damages. Thus, developing techniques for identification and mapping of weeds is necessary for a proper precision agriculture adoption. Such weed identification for site-specific management is difficult when the main crop is already established in the field. This study shows the possibility of differentiating sugarcane plants from weeds by the spectral behaviour of the leaves. The performance of two modelling methods, SIMCA (soft independent modelling by class analogy) and the RF algorithm (random forest) was compared. The simplification of the Vis-NIR spectrum into only four bands of interest (500e550 nm; 650e750 nm; 1300e1450 nm; and 1800e1900 nm) was verified by demonstrating they had the same differentiation ability as the full visible-near infra-red spectrum. Thus, it was shown that performing the proper band selection and local calibration using a spectral classification approach may allow weed mapping and facilitate localised herbicide application. 650 $aHerbicides 650 $aPrecision agriculture 650 $aSugarcane 650 $aAgricultura de Precisão 650 $aCana de Açúcar 650 $aHerbicida 653 $aClassification models 653 $aPost-emergent herbicides 653 $aRandom forest 653 $aSite-specific management 700 1 $aAMARAL, L. R. do 700 1 $aOLIVEIRA, S. R. de M. 700 1 $aCOUTINHO, M. A. N. 700 1 $aFERREIRA NETTO, C. 773 $tBiosystems Engineering$gv. 190, p. 41-46, 2020.
Download
Esconder MarcMostrar Marc Completo |
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
Registros recuperados : 6 | |
2. | | ROSA, H. J. A.; AMARAL, L. R. do; MOLIN, J. P.; CANTARELLA, H. Sugarcane response to nitrogen rates, measured by a canopy reflectance sensor. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 50, n. 9, p. 840-848, set. 2015. Título em português: Resposta da cana?de?açúcar a doses de nitrogênio estimada por sensor de refletância do dossel.Biblioteca(s): Embrapa Unidades Centrais. |
| |
6. | | PEREIRA, F. R. da S.; REIS, A. A. dos; FREITAS, R. G.; OLIVEIRA, S. R. de M.; AMARAL, L. R. do; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; LAMPARELLI, R. A. C.; MORO, E.; MAGALHÃES, P. S. G. Imputation of missing parts in UAV orthomosaics using PlanetScope and Sentinel-2 data: a case study in a grass-dominated área. International Journal of Geo-Information, v. 12, n. 2, 41, Feb. 2023.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
Biblioteca(s): Embrapa Agricultura Digital. |
| |
Registros recuperados : 6 | |
|
Nenhum registro encontrado para a expressão de busca informada. |
|
|