Delving into the porosity domain continuum in hardwood growth rings: What can we learn from computer vision wood identification models?

Hardwood porosity domains (diffuse-, semi-ring-, and ring-porosity) exist along a spectrum with some taxa embodying only one porosity domain and others spanning more than one. A cascading model scheme involving a root-level porosity classifier and second-level taxonomical classifiers might be useful for mitigating reductions in the predictive accuracy of North American computer vision wood identification (CVWID) models when the number of classes increases. Thus far, the porosity classifier has been trained on images covering the breadth of the porosity spectrum. By reducing ambiguity near the boundaries of porosity domains, training the root classifier only on taxa that are quintessentially diffuse-, semi-ring, and ring-porous might produce equivalent or better results. In this study, a two-class (diffuse- and ring-porous) model and a three-class (diffuse-, semi-ring-, and ring-porous) model were trained on specimens only from taxa with quintessentially idealized porosity and tested on specimens with and without idealized porosity. Results showed perfect predictive accuracy for both models when tested on in-model taxa but showed lower accuracy on datasets with non-ideal porosity with all misclassifications being anatomically sensible. In addition, the results showed remarkable similarities between CVWID models and humans in how they “apply” the concept of discrete porosity domains to a real-world continuum.