In this paper we study the possibility to use the artificial neural networks
for trees classification based on real and approximated values of the sap flow
density flux describing water transport in trees. The data sets were generated
by means of a new tree monitoring system TreeTalker©. The Fourier seriesbased model is used for fitting the data sets with periodic patterns. The multivariate regression model defines the functional dependencies between
sap flow density and temperature time series. The paper shows that Fourier coefficients can be successfully used as elements of the feature vectors required to solve different classification problems. Here we train multilayer neural
networks to classify the trees according to different types of classes. The
quality of the developed model for prediction and classification is verified by
numerous numerical examples.