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Softwoods and Hardwoods

Softwoods and Hardwoods

Softwoods and hardwoods, derived from different types of trees, are distinguished by their leaf structures and cellular compositions, each serving unique purposes in construction and manufacturing. Softwoods come from cone-bearing trees with needle-like leaves and are predominantly composed of longitudinal cells called tracheids and a smaller proportion of radial cells known as rays. Due to their cellular structure, softwoods are commonly used in construction for structural frames, sheathing,...

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Updated: Jun 20, 2026

A Technical Perspective in Modern Tree-ring Research - How to Overcome Dendroecological and Wood Anatomical Challenges

A Technical Perspective in Modern Tree-ring Research - How to Overcome Dendroecological and Wood Anatomical Challenges

Published on: March 5, 2015

Enabling high-throughput quantitative wood anatomy through a dedicated pipeline.

Jan Van den Bulcke¹, Louis Verschuren^2,3, Ruben De Blaere^2,4

¹UGent-Woodlab, Department of Environment, Ghent University, Coupure Links 653, 9000, Gent, Belgium. Jan.VandenBulcke@UGent.be.

|February 5, 2025

Summary

This study introduces a semi-automated pipeline for high-throughput wood anatomy analysis. It uses robotics and deep learning to digitize and analyze tree ring data, enabling faster environmental information extraction from wood.

Keywords:

Deep learning Forest ecology Gigapixel imaging Image stitching Increment cores Quantitative wood anatomy Robotic sander Wood discs

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Last Updated: Jun 20, 2026

A Technical Perspective in Modern Tree-ring Research - How to Overcome Dendroecological and Wood Anatomical Challenges

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Published on: March 5, 2015

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Published on: September 22, 2023

Author Spotlight: Innovative Device Development for Advancing Dendroecology and Wood Anatomy Research

Author Spotlight: Innovative Device Development for Advancing Dendroecology and Wood Anatomy Research

Published on: September 27, 2024

Area of Science:

Dendrochronology
Wood Anatomy
Image Analysis

Background:

Trees store historical environmental data in their wood structure.
Conventional wood surface analysis is manual, time-consuming, and limits high-throughput studies.
Digitization and feature segmentation require complex, multi-step processes.

Purpose of the Study:

To develop a semi-automated, high-throughput pipeline for wood sample preparation, gigapixel imaging, and anatomical analysis.
To overcome limitations of conventional methods in analyzing wood anatomical features.
To enable rapid and detailed analysis of large wood surfaces for ecological and climatological research.

Main Methods:

A robotic system for automated wood surface preparation using sandpaper.
A custom-built, open-source Gigapixel Woodbot for automated, high-resolution imaging of wood surfaces.
A Python-based deep learning routine (YOLOv8) for automated quantification of wood anatomical features like vessels and rays.

Main Results:

Successfully digitized beech wood discs (30-35 cm diameter) and increment cores at 2.25 µm resolution.
Automated quantification of up to 13 million vessels and rays on full disc surfaces.
Generated detailed pith-to-bark profiles of vessel density from increment core data.

Conclusions:

The developed pipeline significantly accelerates the analysis of wood anatomical features.
Enables high-detail analysis on large wood surfaces, facilitating robust testing of ecological and climatological hypotheses.
Provides a scalable solution for researchers needing to analyze numerous wood samples with high precision.