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• W2283055605 abstract "Field research was conducted to determine the potential of multispectralclassi¯cation of late-season grass weeds in wheat. Several classi¯cation tech-niques have been used to discriminate di®erences in re°ectance between wheat,and wild oat, canarygrass, ryegrass and rabbit foot in the 400-900 nm spectrum,and to evaluate the accuracy performance for a spectral signature classi¯cationinto the plant species or group to which it belongs. Fisher linear discriminantanalysis, nonparametric functional discriminant analysis and several neural net-works have been applied, either with a preliminary principal component analysisor not and in di®erent scenarios. Fisher linear discriminant analysis, feedforwardneural networks and one-layer neural network, all of them with a principal com-ponent analysis, showed classi¯cation percentages of between 90% and 100 %.Generally, a preliminary computation of the most relevant principal componentsconsiderably improves the correct classi¯cation percentage. These results arepromising because wild oat and ryegrass, two of the most problematic, clearlypatchy and expensive to control weeds in wheat, could be successfully discrim-inated from wheat in the 400-900 nm range. Our results suggest that mappinggrass weed patches in wheat could be feasible with image analysis working forreal-time and high-resolution satellite imagery acquired in mid-May under ourconditions." @default.
• W2283055605 created "2016-06-24" @default.
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• W2283055605 date "2007-01-01" @default.
• W2283055605 modified "2023-09-26" @default.
• W2283055605 title "Multispectral classification of grass weeds and wheat (Triticum durum) crop using linear and nonparametric functional discriminant analysis, and neural networks" @default.
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