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Identification of botanical specimens using artificial neural networks

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1 Author(s)
Clark, J.Y. ; Dept. of Comput., Surrey Univ., Guildford, UK

This work describes a method of training an artificial neural network, specifically a multilayer perceptron (MLP), to identify plants using morphological characters collected from herbarium specimens. A practical methodology is presented to enable taxonomists to use neural networks as advisory tools for identification purposes, by collating results from a population of neural networks. A comparison is made between the ability of the neural network and that of other methods for identification by means of a case study in the ornamental tree genus Tilia L. (Tiliaceae). In particular, a comparison is made with taxonomic keys generated by means of the DELTA system, a suite of programs commonly used by botanists for that purpose. In this study, the MLP was found to perform better than the DELTA key generator.

Published in:

Computational Intelligence in Bioinformatics and Computational Biology, 2004. CIBCB '04. Proceedings of the 2004 IEEE Symposium on

Date of Conference:

7-8 Oct. 2004