Phyllometric parameters and artificial neural networks for the identification of Banksia accessions

Taxonomic identification is traditionally carried out with dichotomous keys, or at least computer-based identification keys, often on the basis of subjective visual assessment and frequently unable to detect small differences at subspecies and varietal ranks. The aims of the present work were to (1) clearly discriminate a wide group of accessions (species,subspeciesandvarieties)belongingtothegenusBanksiaonthebasisof14phyllometricparametersdeterminedby imageanalysisoftheleaves,and(2)unequivocallyidentifytheaccessionswitharelativelysimpleback-propagationneural- network (BPNN) architecture (single hidden layer) in order to develop a complementary method for fast botanical identification. The results indicate that this kind of network could be effectively and successfully used to discriminate amongBanksiaaccessions,astheBPNNenableda93%unequivocalandcorrectsimultaneousidentification.OurBPNNhad theadvantageofbeingabletoresolvesubtleassociationsbetweencharacters,andofmakingincompletedata(i.e.absenceof Banksia flowerparameterssuchasthecolourorsizeofstyles)usefulinspeciesdiagnostics.Thismethodisrelativelyuseful;it iseasytoexecuteasnoparticularcompetencesarenecessary,equipmentislowcost(scannerconnectedtoaPCandsoftware available as freeware) and data acquisition is fast and effective.

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