Data/hora: 10/06/2024 02:29:14

Biblioteca(s): 	Embrapa Milho e Sorgo.	
Data corrente: 	27/10/2008	
Data da última atualização: 	24/05/2018	
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.	
Título: 	A classification methodology for the risk of weed infestation using fuzzy logic.	
Ano de publicação: 	2008	
Fonte/Imprenta: 	Weed Research, Oxford, v. 48, n. 5, p. 470-479, 2008.	
Idioma: 	Inglês	
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.	
Thesaurus Nal: 	fuzzy logic; geostatistics; spatial data.	
Categoria do assunto: 	--	
Registro original: 	Embrapa Milho e Sorgo (CNPMS)	
Exemplares: 	CNPMS - (20974-1) - UPC - AP - DD -  -  -  - DIS