Abstract
This paper considers the problem of creating a model of a convolutional neural network for recognizing tree species from the image of a trunk for ground-based lidar taxation of forest stands. To increase the probability of recognition, it is proposed to use a telegram bot for augmentation of the training set. Training, selection and comparison of convolutional neural network models was performed. A telegram bot has been created that allows you to automate the collection of images of the training sample. The study opens a cycle of works on modeling the carbon balance of forest plantations.
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Terekhov, V., Zabelina, V., Savchenko, G., Chumachenko, S. (2022). Classification of Tree Species by Trunk Image Using Conventional Neural Network and Augmentation of the Training Sample Using a Telegram-Bot. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y., Klimov, V.V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research V. NEUROINFORMATICS 2021. Studies in Computational Intelligence, vol 1008. Springer, Cham. https://doi.org/10.1007/978-3-030-91581-0_28
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