  Computer vision systems for wood identification have the potential to empower
both producer and consumer countries to combat illegal logging if they can be
deployed effectively in the field. In this work, carried out as part of an
active international partnership with the support of UNIDO, we constructed and
curated a field-relevant image data set to train a classifier for wood
identification of $15$ commercial Ghanaian woods using the XyloTron system. We
tested model performance in the laboratory, and then collected real-world field
performance data across multiple sites using multiple XyloTron devices. We
present efficacies of the trained model in the laboratory and in the field,
discuss practical implications and challenges of deploying machine learning
wood identification models, and conclude that field testing is a necessary step
- and should be considered the gold-standard - for validating computer vision
wood identification systems.
