📘 Read the full study in Wood and Fiber Science, Vol. 55 (2023): https://doi.org/10.22382/wfs-2023-14 or contact Dr. Joe Dahlen for more information.
In the forest products industry, understanding wood variability is essential for optimizing product performance and value. Traditionally, this involves labor-intensive measurements and destructive sampling. However, A 2023 study by Raut and Dahlen evaluates a different and novel option: image analysis of cross-sectional disks from longleaf pine.
The researchers developed a method to machine and image green wood disks, then used custom algorithms to extract key metrics—wood and bark volume, diameter, disk shape, and compression wood presence. Their image-based measurements closely matched traditional methods (R² > 0.96), and they successfully identified severe compression wood in over 98% of defect-containing trees.
🌲For forestry professionals, this means:
- Faster, non-destructive wood quality assessments
- Digital records for long-term analysis and model validation
- New insights into how tree defects affect wood properties
As imaging and machine learning technologies advance, this approach could become a standard tool for evaluating wood quality in the field and at the mill—helping to improve product consistency, reduce waste, and enhance forest value.
