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)...
| Main Authors: | , , , , , |
|---|---|
| Format: | Journal Article |
| Published: |
2009
|
| Online Access: | http://hdl.handle.net/20.500.11937/4681 |
| _version_ | 1848744584767602688 |
|---|---|
| author | Messina, G. Pandolfi, C. Mugnai, S. Azzarello, E. Dixon, Kingsley Mancuso, S. |
| author_facet | Messina, G. Pandolfi, C. Mugnai, S. Azzarello, E. Dixon, Kingsley Mancuso, S. |
| author_sort | Messina, G. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. © CSIRO 2009. |
| first_indexed | 2025-11-14T06:03:48Z |
| format | Journal Article |
| id | curtin-20.500.11937-4681 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:03:48Z |
| publishDate | 2009 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-46812017-09-13T14:47:33Z Phyllometric parameters and artificial neural networks for the identification of Banksia accessions Messina, G. Pandolfi, C. Mugnai, S. Azzarello, E. Dixon, Kingsley Mancuso, S. 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. © CSIRO 2009. 2009 Journal Article http://hdl.handle.net/20.500.11937/4681 10.1071/SB08003 restricted |
| spellingShingle | Messina, G. Pandolfi, C. Mugnai, S. Azzarello, E. Dixon, Kingsley Mancuso, S. Phyllometric parameters and artificial neural networks for the identification of Banksia accessions |
| title | Phyllometric parameters and artificial neural networks for the identification of Banksia accessions |
| title_full | Phyllometric parameters and artificial neural networks for the identification of Banksia accessions |
| title_fullStr | Phyllometric parameters and artificial neural networks for the identification of Banksia accessions |
| title_full_unstemmed | Phyllometric parameters and artificial neural networks for the identification of Banksia accessions |
| title_short | Phyllometric parameters and artificial neural networks for the identification of Banksia accessions |
| title_sort | phyllometric parameters and artificial neural networks for the identification of banksia accessions |
| url | http://hdl.handle.net/20.500.11937/4681 |