Benchmarking YOLO models for forest species identification from macroscopic wood images
A comparative study of 15 models from the YOLOv8, YOLO11, and YOLO26 families for automated classification of 73 wood species, with t-SNE separability analysis and out-of-distribution detection.
Why automate identification?
Illegal logging
Enforcement relies on rapidly identifying seized wood species — a process that currently demands scarce specialists.
Shortage of anatomists
Few professionals master wood anatomy. Samples are sent via courier for remote analysis, causing delays and inaccuracies.
Field-ready solution
A model deployed on a smartphone allows environmental agents to identify species on-site, generating instant reports.
Datasets and models
Combined datasets
15 models compared
All trained with mosaic augmentation · 448×448 px · 70/15/15 split
Model performance
YOLO26m achieved the highest overall accuracy on the unified 73-species dataset. Detailed separability and misclassification analysis is available in the t-SNE Viewer.
YOLO26m
YOLO26 family · medium variant