Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

International audienceAn algorithm to automatically detect and measure knots in CT images of softwood beams was developed. The algorithm is based on the use of 3D con- nex components and a 3D distance transform constituting a new approach for knot diameter measurements. The present work was undertaken with the objective to automatically and non-destructively extract the distributions of knot characteristics within trees. These data are valuable for further studies related to tree development and tree architecture, and could even contribute to satisfying the current demand for automatic species identification on the basis of CT images. A review of the literature about automatic knot detection in X-ray CT images is provided. Relatively few references give quantitatively accurate results of knot measurements (i.e., not only knot localisation but knot size and incli- nation as well). The method was tested on a set of seven beams of Norway spruce and silver fir. The outputs were compared with manual measurements of knots performed on the same images. The results obtained are promising, with detection rates varying from 71 to 100%, depending on the beams, and no false alarms were reported. Particular attention was paid to the accuracy obtained for automatic measurements of knot size and inclination. Comparison with manual measurements led to a mean R2 of 0.86, 0.87, 0.59 and 0.86 for inclination, maximum diameter, length and volume, respectively

Simulating Wood Properties in the Context of a Growth and Yield Model for Planted Douglas-fir

Forest growth and yield models are critical to supporting decision making in forestry, but often lack considerations for wood properties. The feasibility of simulating wood properties in the context of a Douglas-fir individual tree growth and yield model was evaluated. This assessment explored the effect of predicted sapwood width, stem taper, branch size and juvenile wood proportion on recovery of utility poles and sawn lumber. Results from the assessment indicated wood properties can be simulated in the context of growth and yield modeling systems with biologically reasonable results. The same assessment indicated recovery of wood products may be overestimated in the absence of penalties for stem defects. Two critical Douglas-fir wood attributes, knot geometry and wood density, were targeted for modeling after the feasibility analysis. A model for Douglas-fir knot pith curvature was developed based on predicted branch angle and growth ring position over time. Based on the predicted knot pith curvature, knot geometry could be implied from branch radius predicted at the stem surface. Medical computed tomography was identified as one way to rapidly and efficiently estimate Douglas-fir wood density from the proxy of X-ray attenuation. In support of deploying this technology, the effect of wood moisture content and other scanner settings on the relationship of X-ray attenuation and wood density was modeled. Results indicated that X-ray attenuation explains most of the variation in predicting wood density, but moisture content and other scanner settings have a small but significant effect. A number of knowledge gaps should be resolved to sufficiently and robustly simulate wood properties in the context of Douglas-fir growth and yield simulation. Models for sawing simulation and for addressing deficits in existing wood properties models should be an important focus for future research in this field

A Machine-Learning Approach for Classifying Defects on Tree Trunks using Terrestrial LiDAR

International audienceThree-dimensional data are increasingly prevalent in forestry thanks to terrestrial LiDAR. This work assesses the feasibility for an automated recognition of the type of local defects present on the bark surface. These sin-gularities are frequently external markers of inner defects affecting wood quality, and their type, size, and frequency are major components of grading rules. The proposed approach assigns previously detected abnormalities in the bark roughness to one of the defect types: branches, branch scars, epi-cormic shoots, burls, and smaller defects. Our machine learning approach is based on random forests using potential defects shape descriptors, including Hu invariant moments, dimensions, and species. The results of our experiments involving different French commercial species, oak, beech, fir, and pine showed that most defects were well classified with an average F 1 score of 0.86

Robust Knot Segmentation by Knot Pith Tracking in 3D Tangential Images

International audienceThis paper proposes a fast, accurate and automatic method to segment wood knots from images obtained by X-Ray Computed Tomography scanner. The wood knot segmentation is a classical problem where the most popular segmentation techniques produce unsatisfactory results. In a previous work, a method was developed to detect knot areas and an approach was proposed to segment the knots. However this last step is not entirely satisfactory in the presence of sapwood. This paper presents a novel approach for knot segmentation, based on the original idea considering slices tangential to the growth rings. They allow to track the knot from the log pith to the bark. Knots are then segmented by detecting discrete ellipses in each slice. A complete implementation is proposed on the TKDetection software available online