Artificial neural networks for species identification by taxonomists.
This paper is a study of the value of applying artificial neural networks (ANNs), specifically a multilayer perceptron (MLP), to identification of higher plants using morphological characters collected by conventional means. A practical methodology is thus demonstrated to enable botanical or zoological taxonomists to use ANNs as advisory tools for identification purposes. A comparison is made between the ability of the neural network and that of traditional methods for plant identification by means of a case study in the flowering plant genus Lithops N.E. Brown (Aizoaceae). In particular, a comparison is made with taxonomic keys generated by means of the DELTA system. The ANN is found to perform better than the DELTA key generator, for conditions where the available data is limited, and species relatively difficult to distinguish.
Clark
Jonathan Y
JY
Department of Computing, University of Surrey, Guildford, Surrey GU2 7XH, UK. j.y.clark@surrey.ac.uk
D035722:Aizoaceae / Q000033:anatomy & histology* / Q000145:classification*; D001185:Artificial Intelligence; D002965:Classification / Q000379:methods*; D016000:Cluster Analysis; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013045:Species Specificity