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dc.contributor.authorLópez Granados, Francisca-
dc.contributor.authorPeña Barragán, José Manuel-
dc.contributor.authorJurado-Expósito, Montserrat-
dc.contributor.authorFrancisco-Fernández, Mario-
dc.contributor.authorCao, Ricardo-
dc.contributor.authorAlonso-Betanzos, A.-
dc.contributor.authorFontela-Romero, Óscar-
dc.identifier.citationWeed Research 48: 28-37 (2008)en_US
dc.description10 pages, 4 tablesen_US
dc.description.abstractField studies were conducted to determine the potential of multispectral classification of late-season grass weeds in wheat. Several classification techniques have been used to discriminate differences in reflectance between wheat and Avena sterilis, Phalaris brachystachys, Lolium rigidum and Polypogon monspeliensis in the 400–900 nm spectrum, and to evaluate the accuracy of performance for a spectral signature classification into the plant species or group to which it belongs. Fisher's linear discriminant analysis, nonparametric functional discriminant analysis and several neural networks have been applied, either with a preliminary principal component analysis (PCA) or not and in different scenarios. Fisher's linear discriminant analysis, feedforward neural networks and one-layer neural network, all showed classification percentages between 90% and 100% with PCA. Generally, a preliminary computation of the most relevant principal components considerably improves the correct classification percentage. These results are promising because A. sterilis and L. rigidum, two of the most problematic, clearly patchy and expensive-to-control weeds in wheat, could be successfully discriminated from wheat in the 400–900 nm range. Our results suggest that mapping grass weed patches in wheat could be feasible with analysis of real-time and high-resolution satellite imagery acquired in mid-May under these conditions.en_US
dc.description.sponsorshipSpanish Ministry of Education and Science through projects MTM2005-00429 and AGL2005-06180-CO3-02, and by the Xunta de Galicia by project PGIDT05TIC10502PR.en_US
dc.format.extent157338 bytes-
dc.publisherBlackwell Publishingen_US
dc.subjectPatch dynamicsen_US
dc.subjectSite-specific weed managementen_US
dc.subjectSpectral signatureen_US
dc.subjectRemote sensingen_US
dc.subjectAvena sterilisen_US
dc.subjectLolium rigidumen_US
dc.subjectPhalaris brachystachys.en_US
dc.titleMultispectral classification of grass weeds and wheat (Triticum durum) using linear and nonparametric functional discriminant analysis and neural networksen_US
dc.description.peerreviewedPeer revieweden_US
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