Trees classification based on Fourier coefficients of the sapflow density flux

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(C). The Fourier series-based 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.

Authors
Efrosinin D. 1, 2 , Kochetkova I. 2, 3 , Stepanova N.4 , Yarovslavtsev A. 2, 5 , Samouylov K. 2, 3 , Valentini R. 2, 6
Language
English
Pages
109-123
Status
Published
Volume
53
Year
2021
Organizations
  • 1 Johannes Kepler Univ Linz, Linz, Austria
  • 2 Peoples Friendship Univ Russia, RUDN Univ, Moscow, Russia
  • 3 RAS, Fed Res Ctr Comp Sci & Control, Inst Informat Problems, Moscow, Russia
  • 4 RAS, VA Trapeznikov Inst Control Sci, Moscow, Russia
  • 5 Russian Timiryazev State Agr Univ, LAMP, Moscow, Russia
  • 6 Tuscia Univ, Viterbo, Italy
Keywords
TreeTalker monitoring system; Fourier coefficients; neural network; classification of trees
Date of creation
20.07.2021
Date of change
20.07.2021
Short link
https://repository.rudn.ru/en/records/article/record/74648/
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