Pollen texture identification using neural networks

Abstract

The importance of research leading to the automation of pollen identification is briefly outlined. A new technique, neural network analysis, is briefly introduced, and then applied to the determination of light microscope images of pollen grains. The results are compared with some previously published statistical classifiers. Although both types of classifiers may work, the neural network is apparently superior to the statistical methods in three ways: high success rates (100% in this case), small number of samples needed for training, and simplicity of features.