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Australian Systematic Botany Australian Systematic Botany Society
Taxonomy, biogeography and evolution of plants
RESEARCH ARTICLE

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

Giuseppe Messina A B , Camilla Pandolfi A , Sergio Mugnai A C , Elisa Azzarello A , Kingsley Dixon B and Stefano Mancuso A
+ Author Affiliations
- Author Affiliations

A Department of Horticulture, University of Florence, Viale delle Idee 30, 50019 Sesto Fiorentino (FI), Italy.

B School of Plant Biology, University of Western Australia, 35 Stirling Highway, Crawley, WA 6907, Australia.

C Corresponding author. Email: sergio.mugnai@unifi.it

Australian Systematic Botany 22(1) 31-38 https://doi.org/10.1071/SB08003
Submitted: 15 January 2008  Accepted: 19 January 2009   Published: 11 March 2009

Abstract

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, subspecies and varieties) belonging to the genus Banksia on the basis of 14 phyllometric parameters determined by image analysis of the leaves, and (2) unequivocally identify the accessions with a relatively simple back-propagation neural-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 among Banksia accessions, as the BPNN enabled a 93% unequivocal and correct simultaneous identification. Our BPNN had the advantage of being able to resolve subtle associations between characters, and of making incomplete data (i.e. absence of Banksia flower parameters such as the colour or size of styles) useful in species diagnostics. This method is relatively useful; it is easy to execute as no particular competences are necessary, equipment is low cost (scanner connected to a PC and software available as freeware) and data acquisition is fast and effective.


Acknowledgements

The authors thank Kings Park and Botanic Garden staff (Dr Matthew Barrett, Russell Barrett, Mr Bob Dixon) and the institution for hosting this project (providing plant material, scanner, travelling expenses) and for assistance with the identification of Banksia; Mr and Mrs Collins, owners of ‘The Banksia Farm’, for access to their collection of Banksia species from eastern Australia.


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