Wood identification of (Endl.) Oerst based on microscopic features and CTGAN-enhanced explainable machine learning models.

Introduction: Accurate and fast identification of wood at the species level is critical for protecting and conserving tree species resources. The current identification methods are inefficient, costly, and complex.

Methods: A wood species identification model based on wood anatomy and using the genus wood cell geometric dataset was proposed.