Identification of botanical specimens using artificial neural networks
Clark, JY (2004) Identification of botanical specimens using artificial neural networks PROCEEDINGS OF THE 2004 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY. 87 - 94.
SRF002518.pdf - Published Version
his paper 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.
|Uncontrolled Keywords:||Science & Technology, Life Sciences & Biomedicine, Technology, Biochemical Research Methods, Computer Science, Artificial Intelligence, Computer Science, Interdisciplinary Applications, Biochemistry & Molecular Biology, Computer Science, herbarium specimens, multilayer perceptrons, neural network applications, taxonomic keys, Tilia|
|Divisions:||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Communication Systems Research|
|Depositing User:||Melanie Hughes|
|Date Deposited:||05 Oct 2010 13:22|
|Last Modified:||23 Sep 2013 18:38|
Actions (login required)
Downloads per month over past year