Authors: BRESSAN, G M¹; KOENIGKAN, L V²; OLIVEIRA, V A¹; CRUVINEL, P E³; KARAM, D⁴

Source: Weed Research, Volume 48, Number 5, October 2008 , pp. 470-479(10)

Publisher: Wiley-Blackwell

Abstract:

Summary

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.

Keywords: fuzzy logic; geostatistics; weed infestation; weed maps; map analysis; patch; pattern; spatial data

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1365-3180.2008.00647.x

Affiliations: 1: Departamento de Engenharia Elétrica, Universidade de São Paulo, Sao Carlos, Brazil 2: Embrapa Informação Tecnológica, Empresa Brasileira de Pesquisa Agropecuária, Brasília, Brazil 3: Embrapa Instrumentação Agropecuária, Empresa Brasileira de Pesquisa Agropecuária, São Carlos, Brazil 4: Embrapa Milho e Sorgo, Empresa Brasileira de Pesquisa Agropecuária, Sete Lagoas, Brazil

Publication date: 2008-10-01

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