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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
18/12/2019 |
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Data da última atualização: |
17/02/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SOUZA, M. F. de; AMARAL, L. R. do; OLIVEIRA, S. R. de M.; COUTINHO, M. A. N.; FERREIRA NETTO, C. |
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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. |
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Título: |
Spectral differentiation of sugarcane from weeds. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Biosystems Engineering, v. 190, p. 41-46, 2020. |
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DOI: |
https://doi.org/10.1016/j.biosystemseng.2019.11.023 |
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Idioma: |
Inglês |
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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. |
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Palavras-Chave: |
Classification models; Post-emergent herbicides; Random forest; Site-specific management. |
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Thesagro: |
Agricultura de Precisão; Cana de Açúcar; Herbicida. |
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Thesaurus Nal: |
Herbicides; Precision agriculture; Sugarcane. |
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Categoria do assunto: |
-- |
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Marc: |
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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.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
