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| 1. |  | MILLER, A. M.; FIGUEIREDO, J. E. F.; LINDE, G. A.; COLAUTO, N. B.; PACOLA-MEIRELLES, L. D. Characterization of the inaA gene and expression of ice nucleation phenotype in Pantoea ananatis isolates from Maize White Spot disease. Genetics and Molecular Research, Ribeirão Preto, v. 15, n. 1, p. 1-8, 2016.| Biblioteca(s): Embrapa Milho e Sorgo. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Milho e Sorgo. |
|
Data corrente: |
27/10/2008 |
|
Data da última atualização: |
24/05/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
Internacional - A |
|
Autoria: |
BRESSAN, G. M.; KOENIGKAN, L. V.; OLIVEIRA, V. A.; CRUVINEL, P. E.; KARAM, D. |
|
Afiliação: |
G. M. Bressan, USP; L. V. Koenigkan, Embrapa Informação Tecnológica; V. A. Oliveira, USP; PAULO ESTEVAO CRUVINEL, CNPDIA; DECIO KARAM, CNPMS. |
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Título: |
A classification methodology for the risk of weed infestation using fuzzy logic. |
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Ano de publicação: |
2008 |
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Fonte/Imprenta: |
Weed Research, Oxford, v. 48, n. 5, p. 470-479, 2008. |
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Idioma: |
Inglês |
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Conteúdo: |
Despite modern weed control practices, weeds continue to be a threat to agricultural production. Considering the variability of weeds, a classification methodology for the risk of infestation in agricultural zones using fuzzy logic is proposed. The inputs for the classification are attributes extracted from estimated maps for weed seed production and weed coverage using kriging and map analysis and from the percentage of surface infested by grass weeds, in order to account for the presence of weed species with a high rate of development and proliferation. The output for the classification predicts the risk of infestation of regions of the field for the next crop. The risk classification methodology described in this paper integrates analysis techniques which may help to reduce costs and improve weed control practices. Results for the risk classification of the infestation in a maize crop field are presented. To illustrate the effectiveness of the proposed system, the risk of infestation over the entire field is checked against the yield loss map estimated by kriging and also with the average yield loss estimated from a hyperbolic model. |
|
Palavras-Chave: |
Map analysis; Patch; Pattern; Weed infestation; Weed maps. |
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Thesaurus NAL: |
fuzzy logic; geostatistics; spatial data. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01879naa a2200265 a 4500
001 1491410
005 2018-05-24
008 2008 bl uuuu u00u1 u #d
100 1 $aBRESSAN, G. M.
245 $aA classification methodology for the risk of weed infestation using fuzzy logic.$h[electronic resource]
260 $c2008
520 $aDespite modern weed control practices, weeds continue to be a threat to agricultural production. Considering the variability of weeds, a classification methodology for the risk of infestation in agricultural zones using fuzzy logic is proposed. The inputs for the classification are attributes extracted from estimated maps for weed seed production and weed coverage using kriging and map analysis and from the percentage of surface infested by grass weeds, in order to account for the presence of weed species with a high rate of development and proliferation. The output for the classification predicts the risk of infestation of regions of the field for the next crop. The risk classification methodology described in this paper integrates analysis techniques which may help to reduce costs and improve weed control practices. Results for the risk classification of the infestation in a maize crop field are presented. To illustrate the effectiveness of the proposed system, the risk of infestation over the entire field is checked against the yield loss map estimated by kriging and also with the average yield loss estimated from a hyperbolic model.
650 $afuzzy logic
650 $ageostatistics
650 $aspatial data
653 $aMap analysis
653 $aPatch
653 $aPattern
653 $aWeed infestation
653 $aWeed maps
700 1 $aKOENIGKAN, L. V.
700 1 $aOLIVEIRA, V. A.
700 1 $aCRUVINEL, P. E.
700 1 $aKARAM, D.
773 $tWeed Research, Oxford$gv. 48, n. 5, p. 470-479, 2008.
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Embrapa Milho e Sorgo (CNPMS) |
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