The qualitative evaluation of harvested raw logs and sawlogs is mainly based on the quantitative and qualitative evaluation of the visible macroscopic features of the wood. Modern methods allow for the analysis of whole logs by means of computed tomography. These devices can analyze the internal qualitative features of wood that are not visible on the external structures of the logs. The aim of this work was to evaluate the detection accuracy of a CT-scanning device intended for scanning logs on the internal qualitative features of wood using model trunks. Two logs of beech and oak with a length of 4 m were selected for the analysis, based on availability. Qualitative features were identified through computed tomography scanning, visually identified on cut sections, and then manually measured in accordance with applicable legislation. Relatively good agreement was demonstrated for the detected features in terms of identifying their location (dimension in millimeters from the end of the log). For this parameter, the average differences were 0.90% on the beech log and only 1.21% on the oak log. Relatively high accuracy was shown via CT detection of qualitative features in the beech section (with average differences in dimensions of only 3.5%). In the case of the oak log, the dimensions of the quality features were significantly overestimated. These results indicate that CT scanning technology may have a problem with some hardwood species. It was primarily developed for coniferous tree species, and software algorithms are, therefore, not yet fully adapted to the precise detection of the dimensions of individual quality features. Despite the detected differences, it was confirmed that the CT technology of scanning harvested wood can have a fundamental impact on optimization procedures in the recovery and processing of wood. Renting a scanning line for a certain capacity of wood volume appears to be a deployment option for forestry operations and smaller wood processing operations. Thus, this technology can become an important factor in improving the economic evaluation of the final production of wood.

The first goal of this paper is to verify the accuracy of four calculation methods of log volume. The tool to achieve this goal is to compare the results of the calculation of the log volume with the real log volume obtained from the three-dimensional reconstruction obtained by computed tomography. The second goal of this paper is to determine the effectiveness of displaying the qualitative features of wood in three-dimensional models of selected pieces of logs of oak, beech, and spruce, which were obtained using computed tomography. It is possible to state that each of the tested calculation methods of wood log volume are applicable in practice. The tested methods achieve excellent accuracy in determining the volume of spruce logs with a small variance of values, and conversely, in the case of beech wood, the tested methods are the most inaccurate with the largest variance of values. When determining the volume of wood logs, we recommend using the calculation method STN 48 0009, because it achieves the best results. Qualitative analysis based on CT scans of internal features can be described as a completely new level of approach to the evaluation of log quality. The performed analysis showed great potential for automatic detection of internal qualitative features in the tested spruce log. In this wood, wood defects are distinguishable by computed tomography. In the case of deciduous oak and beech, the situation is more complicated. The internal structure of these trees overlaps the internal qualitative features of the wood. To accurately detect internal errors in these trees, it will be necessary to perform many comparative tests to achieve optimal results.

This article focuses on the need for digitalization in the forestry and timber sector using information from CT scans of logs. The National Forest Centre (Slovak Republic) operates a unique 3D CT scanner for wooden logs at the Stráž Biotechnology Park. This real-time scanner generates a 3D model of a log, displaying the wood’s internal features/defects. To optimize log-cutting plans effectively, it is necessary to automatically detect and classify these features and defects in real time, leveraging computer vision principles. Artificial intelligence, specifically neural networks, addresses this need by enabling solutions for tasks of this nature. Building a highly efficient neural network for detecting wood features and defects requires creating a database of log scans and training the network on these data. This is a time-intensive process, as it involves manually marking internal features and defects on hundreds of CT scans of various wood types. A functional neural network for detecting internal wood defects represents a significant advancement in sector digitalization, paving the way for further automation and robotization in wood processing. For the forestry sector to remain competitive, efficiently process raw materials, and improve product quality, the effective application of CT scanning technology is essential. This technological innovation aligns the sector more closely with leaders in other fields, such as the automotive, engineering, and metalworking industries.

This work aimed to compare the qualitative assessment of valuable oak logs using the conventional method employed in forestry operations with the evaluation based on image outputs from a CT scanning line. A total of 125 oak logs from southwestern Slovakia were analyzed. Quantitative and qualitative features were measured using both approaches, and the logs were classified into quality classes according to the technical conditions of the STN 48 0056 standard, based on their dimensions. The qualitative assessment of logs using CT images revealed additional internal features that often resulted in the downgrading of logs to lower-quality classes. This method of evaluation increased the frequency of logs classified into lower-quality classes. Nearly half of all evaluated logs were classified into the same quality class by both assessment methods. However, 33% of the logs were classified as one quality class lower when assessed using CT images. This discrepancy can be tentatively attributed to the detection of hidden internal features, as well as the enhanced precision in measurement and feature evaluation enabled by 3D models and cross-sectional images from CT scans. Notably, more than 10% of the logs were classified into a higher quality class based on CT images compared to visual assessment. The economic evaluation based on CT classification was 3769 EUR lower than that based on the conventional method.