Multispectral classification of grass weeds and wheat (Triticum durum) crop using linear and nonparametric functional discriminant analysis, and neural networks
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Autores: Francisca López Granados, José Manuel Peña Barragán, Montserrat Jurado Expósito, Mario Francisco-Fernández
, Ricardo Cao Abad , Amparo Alonso Betanzos
- Localización: XXX Congreso Nacional de Estadística e Investigación Operativa y de las IV Jornadas de Estadística Pública: actas, 2007, ISBN 978-84-690-7249-3
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
Field research was conducted to determine the potential of multispectral classi¯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¯cation into the plant species or group to which it belongs. Fisher linear discriminant analysis, nonparametric functional discriminant analysis and several neural net- works have been applied, either with a preliminary principal component analysis or not and in di®erent scenarios. Fisher linear discriminant analysis, feedforward neural 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 components considerably improves the correct classi¯cation percentage. These results are promising because wild oat and ryegrass, two of the most problematic, clearly patchy and expensive to control weeds in wheat, could be successfully discrim- inated from wheat in the 400-900 nm range. Our results suggest that mapping grass weed patches in wheat could be feasible with image analysis working for real-time and high-resolution satellite imagery acquired in mid-May under our conditions.
