The article presents the constraining factors that limit the increase in the efficiency of logging production by modern multi-operation machines operating on the Scandinavian cut-to-length technology in the felling phase, namely the selection and registration of wood species. The factors for creating a complete architecture of a fully connected neural network are given, the dependence of the prediction accuracy of a fully connected neural network on a test sample on the size of the training data set, an image of the dependence of the prediction accuracy on the number of trees in the random forest method for image classification is shown. Classifiers for the problem of determining the species of a tree trunk from an image based on the methods of a fully connected neural network and random forest have been studied. The considered classifiers are written in the Python programming language using the tensorflow library. The PyCharm Community cross-platform integrated development environment was used to write the code. For fully connected neural networks, a sufficient number of images and a test sample size were established for training, using tree trunk breed class labels as target values. As a result of the research, the required number of images was determined for training fully connected neural networks when using tree trunk breed class labels with high prediction accuracy as target values. Dependences of the prediction accuracy of a fully connected neural network on a test set on the size of the training data set are constructed.