The need for accurate and rapid field identification of wood to combat illegal logging around the world is outpacing the ability to train personnel to perform this task. Despite increased interest in non-anatomical (DNA, spectroscopic, chemical) methods for wood identification, anatomical characteristics are the least labile data that can be extracted from solidwood products, independent of wood processing (sawing, drying, microbial attack). Wood identification using anatomical characteristics is thus still a viable approach to the wood identification problem, and automating the process of identification is an attractive and plausible solution. The undisputed increase of computer power and image acquisition capabilities, along with the decrease of associated costs, suggests that it is time to move toward non-human based automated wood identification systems and methods. This article briefly reviews the foundations of image acquisition and processing in machine vision systems and overviews how machine vision can be applied to wood identification.
The terminology related to axial resin canals in conifers is briefly reviewed, standard terms are clarified and a new term is proposed. The definitions proposed are intended primarily for light microscopic observations. All the cells and spaces of an axial resin canal as differentiated from the axial tracheids are collectively referred to as the resin canal complex. The resin canal is the intercellular space itself, and the epithelium is the uniseriate layer of cells lining the canal. We propose the term subsidiary cells to include all cells exterior to the epithelium, which may be subsidiary parenchyma and /or strand tracheids.
With the adoption of a number of anti-illegal logging laws, treaties, memoranda, and international agreements around the world, there is broad and renewed interest in wood identification, especially in the field at the macroscopic level. In response to this interest, and to begin to fill an obvious gap in the corpus of wood anatomical reference material, we review several prominent English-language publications on macroscopic wood identification in order to form a list of characters. We compile characters and organize them in the spirit of the IAWA lists for hardwood and softwood microscopic identification, present the state of the art as it exists, attempt to reconcile the different sets of definitions, characters, and character states, then present our proposed working-list. It is our intent with this publication to open an international discussion regarding the standardization of macroscopic wood identification features, and it is our hope that such a discussion can include critical works from the non-English literature. We also call for an illustrated glossary to accompany the proposed list. A standard lexicon to describe wood at the macroscopic level will simplify the preparation of identification documents and permit the ready translation of keys and other references for easy use and deployment around the world.
A description of the occurrence and structure of “ray-intrusive” laticifers in the rays of species of Croton section Cyclostigma is provided. The systematic significance of laticifers within Croton section Cyclostigma is briefly discussed in relation to the section’s known production of red latex, commonly called “dragon’s blood”. A developmental hypothesis is offered and discussed in the context of the assumption that all laticifers in wood rays are non-articulated.
Three microscopic characters were evaluated for the identification of Pinus contorta and Pinus ponderosa. The tangential diameter of the resin canals, including the epithelium, was compared to the tangential diameter of the entire resin canal complex. The latter measurement was shown to give diagnostic results for these species. Data from the examination of ray composition do not support previously published methods for separating P. contorta and P. ponderosa. The presence or absence of small elongate crystals in the subsidiary parenchyma of the resin canal complexes was shown to be the most powerful diagnostic character for separating the wood of these species.
One rate-limiting factor in the fight against illegal logging is the lack of powerful, affordable, scalable wood identification tools for field screening. Computer vision wood identification using smartphones fitted with customized imaging peripherals offers a potential solution, but to date, such peripherals suffer from one or more weaknesses: low image quality, lack of lighting control, uncontrolled magnification, unknown distortion, and spherical aberration, and/or no access to or publication of the system design. To address cost, optical concerns, and open access to designs and parameters, I present the XyloPhone, a 3D printed research quality macroscopic imaging attachment adaptable to virtually any smartphone. It provides a fixed focal distance, exclusion of ambient light, selection of visible light or UV illumination, uses the lens from a commercially available loupe, is powered by a rechargeable external battery, is fully open-sourced, at a price point of less than USD 110 is a highly affordable tool for the laboratory or the field, and can serve as the foundational hardware for a scalable field-deployable computer vision wood identification system.
Big-leaf mahogany is the world’s most valuable widely traded tropical timber species and Near Infrared Spectroscopy (NIRS) has been applied as a tool for discriminating its wood from similar species using multivariate analysis. In this study four look-alike timbers of Swietenia macrophylla (mahogany or big-leaf mahogany), Carapa guianensis (crabwood), Cedrela odorata (cedar or cedro) and Micropholis melinoniana (curupixá) have been successfully discriminated using NIRS and Partial Least Squares for Discriminant Analysis using solid block and milled samples. Species identification models identified 155 samples of S. macrophylla from 27 countries with a correct classification rate higher than 96.8%. For these specimens, the NIRS spectrum variation was more powerful for species identification than for determining provenance of S. macrophylla at the country level.