The sawmill industry traditionally relies on visual grading of roundwood, which primarily reflects commercial value rather than the mechanical performance of the resulting sawn timber. This study evaluates the potential of basic non-destructive methods for predicting the physical and mechanical properties of sawn timber based on sawlog characteristics. Sixty sawlogs of Norway spruce (Picea abies L. [H.] Karst.) were assessed using visual grading, manual evaluation of knot parameters, and vibro-acoustic measurements based on the frequency resonance method. The logs were sawn into 180 centre boards with nominal dimensions of 50 × 160 × 3,000 mm, which were tested for physical and mechanical properties in accordance with EN 408. The results show that visual quality grades of sawlogs are generally poor predictors of the sawn timber mechanical properties. In contrast, strong correlations between MOEDL at log level and MOR (r = 0.75) or MOEglob (r = 0.80) were found. The study highlights the critical importance of using the actual wood density when applying the frequency resonance method. Also, only measurements in longitudinal direction should be performed. Knot-related parameters proved relevant, where the total number of knots showed the best results on the global level (r = –0.68 for MOR). The use of designed sliding windows further improved prediction accuracy. Overall, the findings demonstrate that basic non-destructive methods, especially acoustic measurements combined with selected knot parameters, can effectively support sawlog pre-sorting to produce strength-graded sawn timber. In follow-up research, these basic methods should be combined with the results of CT scanning.
The production of wooden barrels represents a specific segment of the wood-processing industry with high added value, especially in the aging of wine and spirits. Accurate determination of the internal volume of barrels is crucial for quality control, inventory management, production process optimization, and for the beverages themselves during aging. The aim of this study was to design and test a device that enables the measurement of barrel internal volume using a dry digital method. The device eliminates the disadvantages of traditional measurement methods (wet and calculated) and allows rapid, precise, and contactless volume determination. The prototype was tested on a set of new and used oak barrels of various volumes. The results confirmed high measurement accuracy and significant time savings compared to conventional methods.
Determining the quality of wood logs without cutting poses a significant challenge for boosting the productivity of wood suppliers and processors. Our study used 3D Computed Tomography scanning to inspect European Oak wood logs for inequalities. We scanned 26 oak logs with a resolution of 10 × 1 × 1 mm, producing an average of 390 to 430 images per log. These images were visually analysed and categorised into classes: Normal wood, Dead knot, Live knot, Rot, and Split. In the first phase, we investigated classification methods using machine learning for the two categories: non-defective and defective wood. In the second and third phases, we expanded our analysis to include all key defect classes: split, rot, dead and live knots. We utilised Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), k-Nearest Neighbor (k-NN), AdaBoost, and Gradient Boosting (GB). In the initial classification (non-defective and defective wood), we achieved 90% and 93% testing accuracy with DT and RF. Then, we aimed to improve accuracy by replacing the feature extractors and using a bagging voting classifier. The third phase used two feature selection approaches, LASSO feature selection and Mutual Information, and we obtained 88%, 95%, and 90% testing accuracies.
Airborne laser scanning has become a well-established, consolidated, and widely used technology for individual tree detection. In this context, we have developed the geoprocessing tool xTree to allow for the detection of individual tree tops and tree crowns based on a combination of airborne laser scanning data and a priori information consisting of geometric and dendrometric rules. The primary aim of this article is to present the workflow for the detection of individual trees using the xTree tool. A detailed description, flowchart, and scheme are provided for these purposes. The additional aim is to present the performance of this workflow through a case study. The study area covered 2.8 ha of forests in Slovakia. The airborne laser scanning data reached an average density of 18 ±6 points per m2. The ground reference data included 2,119 trees in coniferous, broadleaved, and mixed strata. The enhanced detection algorithm achieved overall correctness of 85 ±2%. The overall completeness from the tree number was 69 ±4%, and the overall completeness from the tree volume was 78 ±2%. Generally, the performance increased with the increasing proportion of coniferous trees and diameter of trees.
This study deals with the application of computed tomography (CT) for the design, application, and analysis of a methodology for identifying selected qualitative characteristics of unedged beech timber (Fagus sylvatica L.). In the context of growing interest in the use of hardwood in load-bearing structures, a methodology has been proposed that enables non-contact assessment of internal defects such as knots, cracks, and fibre deviation. CT scanning has demonstrated the ability to visualize local density variations that correlate with the occurrence of quality defects. Calibration using seven wood species of varying density confirmed a linear relationship between CT image intensity and wood density (r = 0.998). The results show that CT technology has potential not only for sorting roundwood but also for evaluating sawn timber after cutting, thereby contributing to the optimization of production and increasing the added value of wood products. Density-based defect identification provides new opportunities for automated grading using machine learning.
