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A method for the high-throughput analysis of the relative lignin contents of Cryptomeria japonica samples over a wide concentration range (3–73%), independent of the type of chemical pretreatment, was developed by using Fourier transform infrared spectroscopy. First, the assignments of the infrared absorbance related to lignin were reviewed. Then, various chemical treatments, including alkaline, acid, and hydrothermal processes, and a sodium chlorite oxidation treatment, were performed to prepare samples containing a wide range of different lignin contents. Principal component analysis indicated high variability among the chemical treatments in terms of the corresponding lignin contents as well as the resulting changes in the chemical structure of hemicellulose; this conclusion was supported by the loading vectors. The intensity of the key band of lignin at 1508 cm−1 was calculated using the absorbance at 2900 cm−1 as a reference; a reliable calibration curve with an R2 of 0.968 was obtained independent of the chemical treatment performed. This simple and rapid method for determining the lignin content is expected to be widely applicable for optimizing bioethanol production, as well as monitoring biomass degradation processes.

The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science.

Vascular bundles of bamboo are determinants for mechanical properties of bamboo material and for physiological properties of living bamboo. The morphology of vascular bundles reflecting mechanical and physiological functions differs not only within internode tissue but also among different internodes in the culm. Although the distribution of vascular bundle fibers has received much attention, quantitative evaluation of the morphological transformation of vascular bundles associated with spatial distribution patterns has been limited. In this study deep learning models were used to determine quantitative changes in the distribution and morphology of vascular bundles in the culms of moso bamboo ( Phyllostachys pubescens ). A precise model for extracting vascular bundles from cross-sectional images was constructed using the U-Net model. Analyses of extracted vascular bundles from different internodes showed significant changes in vascular bundle distribution and morphology among internodes. Vascular bundles in lower internodes showed outer relative position and larger area than those in upper internodes. Aspect ratio and eccentricity indicate that vascular bundles in internodes near the base have more elliptical morphology, with a long axis in the radial direction. The variational autoencoder model using extracted vascular bundles enabled simulation of the morphological transformation of vascular bundles along with radial direction. These deep learning models enabled highly accurate quantification of vascular bundle morphologies, and will contribute to a further understanding of bamboo development as well as evaluation of the mechanical and physiological properties of bamboo.

Tensile tests of birch cork were performed in the tangential direction. Birch cork in the wet state showed significantly higher extensibility than those in the dried state. The histochemical structure of birch cork was investigated by microscopic observation and spectroscopic analysis. Birch cork cell walls showed a two-layered structure and the inner material bordering cell wall. In transmission electron micrographs, osmium tetroxide stained the outer layer and inner material, whereas potassium permanganate stained the inner layer and inner material. After removal of suberin and lignin, only inner layer remained and Fourier-transformed infrared spectra showed the cellulose I pattern. Polarizing light micrographs indicated that molecular chains in the outer layer and inner material were oriented perpendicular to suberin lamination, whereas those in the inner layer showed longitudinal orientation. These results suggested that the outer layer and inner material mainly consist of suberin, whereas the inner layer and compound middle lamella consist of lignin, cellulose, and other polysaccharides. We hypothesized a hierarchical model of the birch cork cell wall. The lignified cell wall with helical arrangement of cellulose microfibrils is sandwiched between suberized outer layer and inner material. Cellulose microfibrils in the inner layer bear tensile loads. In the wet state, water and cellulose transfer tensile stress. In the dried state, this stress-transferal system functions poorly and fewer cells bear stress. Suberin in the outer layer and inner material may prevent absolute drying to maintain mechanical properties of the bark and to bear tensile stress caused by trunk diameter growth.

Although wood cross sections contain spatiotemporal information regarding tree growth, computer vision-based wood identification studies have traditionally favored disordered image representations that do not take such information into account. This paper describes image partitioning strategies that preserve the spatial information of wood cross-sectional images. Three partitioning strategies are designed, namely grid partitioning based on spatial pyramid matching and its variants, radial and tangential partitioning, and their recognition performance is evaluated for the Fagaceae micrograph dataset. The grid and radial partitioning strategies achieve better recognition performance than the bag-of-features model that constitutes their underlying framework. Radial partitioning, which is a strategy for preserving spatial information from pith to bark, further improves the performance, especially for radial-porous species. The Pearson correlation and autocorrelation coefficients produced from radially partitioned sub-images have the potential to be used as auxiliaries in the construction of multi-feature datasets. The contribution of image partitioning strategies is found to be limited to species recognition and is unremarkable at the genus level.

The anatomical structure of wood is complex and contains considerable information about its specific species, physical properties, growth environment, and other factors. While conventional wood anatomy has been established by systematizing the xylem anatomical features, which enables wood identification generally up to genus level, it is difficult to describe all the information comprehensively. This study apply two computer vision approaches to optical micrographs: the scale-invariant feature transform algorithm and connected-component labelling. They extract the shape and pore size information, respectively, statistically from the whole micrographs. Both approaches enable the efficient detection of specific features of 18 species from the family Fagaceae. Although the methods ignore the positional information, which is important for the conventional wood anatomy, the simple information on the shape or size of the elements is enough to describe the species-specificity of wood. In addition, according to the dendrograms calculated from the numerical distances of the features, the closeness of some taxonomic groups is inconsistent with the types of porosity, which is one of the typical classification systems for wood anatomy, but consistent with the evolution based on molecular phylogeny; for example, ring-porous group Cerris and radial-porous group Ilex are nested in the same cluster. We analyse which part of the wood structure gave the taxon-specific information, indicating that the latewood zone of group Cerris is similar to the whole zone of group Ilex. Computer vision approaches provide statistical information that uncovers new aspects of wood anatomy that have been overlooked by conventional visual inspection.

This paper describes computer vision-based quantitative microscopy and its application toward better understanding species specificity. An image dataset of the Lauraceae family that consists of nine species across six genera was investigated, and structural features were quantified using encoded local features implemented in a bag-of-features framework. Of the algorithms used for feature detection, the scale-invariant feature transform (SIFT) achieved the best performance in species discrimination. In the bag-of-features framework with the SIFT features, each image is represented by a histogram of codewords. The codewords were further analyzed by mapping them to each image to visualize the corresponding anatomical elements. From this analysis, we were able to classify and quantify the modes of aggregation of different combinations of cell elements based on clustered codewords. An analysis of the term frequency–inverse document frequency weights revealed that blob-based codewords are generally shared by all species, whereas corner-based codewords are more species specific.

Woody cells, such as tracheids, fibers, vessels, rays etc., have unique structural characteristics such as nano-scale ultrastructure represented by multilayers, microfibril angle (MFA), micro-scale anatomical properties and spatial arrangement. Simultaneous evaluation of the above indices is very important for their adequate quantification and extracting the effects of external stimuli from them. However, it is difficult in general to achieve the above only by traditional methodologies. To overcome the above point, a new methodological framework combining polarization optical microscopy, fluorescence microscopy, and image segmentation is proposed. The framework was tested to a model softwood species, Chamaecyparis obtusa for characterizing intra-annual transition of MFA and tracheid morphology in a radial file unit. According our result, this framework successfully traced the both characteristics tracheid by tracheid and revealed the high correlation (| r | > 0.5) between S 2 microfibril angles and tracheidal morphology (lumen radial diameter, tangential wall thickness and cell wall occupancy). In addition, radial file based evaluation firstly revealed their complex transitional behavior in transition and latewood. The proposed framework has great potential as one of the unique tools to provide detailed insights into heterogeneity of intra and inter-cells in the wide field of view through the simultaneous evaluation of cells’ ultrastructure and morphological properties.

Hornification of cellulose is a well-known phenomenon that takes place during the drying process. Hornification involves a structural change in the cellulose that restricts the enzyme’s ability to access and saccharify the cellulose. We observed never-dried (ND), pressed (PR), and oven-dried (OD) softwood pulp during saccharification catalyzed by fluorescent labelled cellulase from Trichoderma reesei by confocal laser scanning microscopy. Initially cellulases were observed on outer surfaces and dislocations of all the ND, PR, and OD pulp fibers. It was clearly observed that over time that cellulases penetrated the cell walls of the ND pulp fiber from the outer surface, inner surface and cracks and remained in the cell walls. In contrast, they did not penetrate the cell walls of the OD pulp fiber and instead stayed on the cracks and ends of the shortened fibers. Mannan and xylan were found at dislocations of ND and OD fibers by immunolabelling microscopy. The results suggested that hemicellulose plays an important role in hornification at dislocations.