Identification of botanical specimens using artificial neural networks

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.