DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring Detector

¹Henry Marichal^*, ²Verónica Casaravilla, ³Candice Power, ²Karolain Mello, ²Joaquín Mazarino, ²Christine Lucas, ²Ludmila Profumo, ²Diego Passarella, ¹Gregory Randall,

¹Facultad de Ingeniería, Universidad de la República, Uruguay ²CENUR, Universidad de la República, Uruguay ³Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Denmark

hmarichal93@gmail.com

Data Paper Code Codespaces

DeepCS-TRD

We propose Deep CS-TRD, a new automatic algorithm for detecting tree rings in whole cross-sections. It substitutes the edge detection step of CS-TRD by a deep-learning-based approach (U-Net), which allows the application of the method to different image domains: microscopy, scanner or smartphone acquired, and species (Pinus taeda, Gleditsia triachantos and Salix glauca).

Additionally, we introduce two publicly available datasets of annotated images to the community. The proposed method outperforms state-of-the-art approaches in macro images (Pinus taeda and Gleditsia triacanthos) while showing slightly lower performance in microscopy images of Salix glauca. To our knowledge, this is the first paper that studies automatic tree ring detection for such different species and acquisition conditions.

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Acknowledgements

The experiments presented in this paper used ClusterUY (site: https://cluster.uy). This work was supported by project ANII-FMV-176061. CCP was supported by IRFD (702700133B) and H2020 CHARTER (869471).