The relative abilities of the multilayer perceptron,
radial basis function, asymmetric radial basis function and learning vector
quantization artificial neural networks (ANNs) and two non-neural methods
to identify fungal spores were compared. ANNs were
trained on morphometric data from spores of Pestalotiopsis
spp. and a few species in the related Truncatella and Monochaetia.
The
optimized neural and statistical classifiers had similar identification
success on an unseen data set – between 76·0 and 78·8%
of a
16-species group and between 63·0 and 67·7% of a
19-species group. The relative merits of each classifier are discussed,
as is the
potential of ANNs in mycology.