Automated plant identification using artificial neural networks

Clark, JY (2012) Automated plant identification using artificial neural networks In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2012-05-09 - 2012-05-12, San Diego.

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Abstract

This paper describes a method of training an artificial neural network, specifically a multilayer perceptron (MLP), to act as a tool to help identify plants using morphological characters collected automatically from images of botanical herbarium specimens. A methodology is presented here to provide a practical way for taxonomists to use neural networks as automated identification tools, by collating results from a population of neural networks. A case study is provided using data extracted from specimens of the genus Tilia in the Herbarium of the Royal Botanic Gardens, Kew, UK. A classification accuracy of 44% was achieved on this challenging multiclass problem.

Item Type:	Conference or Workshop Item (Conference Paper)
Divisions :	Faculty of Engineering and Physical Sciences > Computing Science
Authors :	Name Email ORCID Clark, JY
Date :	1 June 2012
DOI :	10.1109/CIBCB.2012.6217250
Contributors :	Contribution Name Email ORCID http://www.loc.gov/loc.terms/relators/EDT jin, Y http://www.loc.gov/loc.terms/relators/PBL IEEE Computation Intelligence Society,
Additional Information :	Copyright 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Depositing User :	Symplectic Elements
Date Deposited :	06 Nov 2012 16:34
Last Modified :	31 Oct 2017 14:51
URI:	http://epubs.surrey.ac.uk/id/eprint/723344

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