deepT – deep neural network algorithms to recognize species and their specific response patterns in stem growth data of TreeNet

deepT investigated times series of stem radius changes with deep neural network (DNN) algorithms in order to recognize tree species, growth patterns and tree water deficit-induced stem shrinkage characteristics. deepT tested different types of DNN algorithms, develops a Python environment for extensive data sets and quantifies the reliability of different approaches to recognize species-specific patterns and judge their potential for making predictions.

deepT is a collaboration bewtween EMPA (Mirko Lukovic, Cellulose & Wood Materials, Empa Dübendorf) and WSL (Roman Zweifel, Forest Dynamics).

Poster shown at the final presentation of this project at WSL, 24 May 2022.