Towards Sustainable North American Wood Product Value Chains, Part I: Computer Vision Identification of Diffuse Porous Hardwoods

Abstract

Availability of and access to wood identification expertise or technology is a critical component for the design and implementation of practical, enforceable strategies for effective promotion, monitoring and incentivisation of sustainable practices and conservation efforts in the forest products value chain. To address this need in the context of the multi-billion-dollar North American wood products industry 22-class, image-based, deep learning models for the macroscopic identification of North American diffuse porous hardwoods were trained for deployment on the open-source, field-deployable XyloTron platform using transverse surface images of specimens from three different xylaria and evaluated on specimens from a fourth xylarium that did not contribute training data. Analysis of the model performance, in the context of the anatomy of the woods considered, demonstrates immediate readiness of the technology developed herein for field testing in a human-in-the-loop monitoring scenario. Also proposed are strategies for training, evaluating, and advancing the state-of-the-art for developing an expansive, continental scale model for all the North American hardwoods.

Opening the black box: explainable deep-learning classification of wood microscopic image of endangered tree species

“…Currently, computer vision is developing rapidly, and there has been a lot of work done in wood macro image classification [ 18 – 20 ]. In forensic wood identification, it is often necessary to provide identification keys, namely the features on which experts base their judgments.…”

Evaluation Of Test Specimen Surface Preparation On Macroscopic Computer Vision Wood Identification

“…Multiple technologies for automated wood identification have been researched to help address this dearth of wood identification experts (Schmitz et al 2020). Powered by advances in machine learning, democratized access to macroscopic imaging tools such as the XyloTron and Xylo-Phone (Ravindran et al 2020;Wiedenhoeft 2020), and cloud-based processing platforms, computer vision wood identification (CVWID, Khalid et al 2008;Hwang and Sugiyama 2021) has been demonstrated to be an effective and affordable technology for automated wood identification at country and continental scales for field screening purposes (Ravindran et al 2018(Ravindran et al , 2019(Ravindran et al , 2020(Ravindran et al , 2022a(Ravindran et al , 2022bde Geus et al 2020;Ar evalo et al 2021).…”