Wood Artificial Intelligence — Knot modeling by Computed Tomography

Wood can serve as long-term carbon sink, and increased use in buildings is a means to reduce the negative effects of climate change. Consequently, it is important to produce sawn timber suitable for buildings, furniture and other products with long lifespans. As more wood is used, it is even more important to use the wood resource efficiently. Industrial computer tomography (CT) scanning of logs is becoming a more widespread tool in sawmills, and it has proven to considerably increase the volume of high-value sawn timber. CT scanning of logs provides information on the interior properties prior to sawing, and this can be considered when optimising the sawing. The most difficult CT-detection problems are the size of knots in sapwood and the transition between sound and dead knot sections. Knot size and whether knots are sound or dead are important factors for sawn timber quality, and the requirements vary between different products. Knot size is a limitation for structural timber since it has negative effects on strength and stiffness. For timber used in visual applications, where the appearance is important, there are additional requirements for sound and dead knots. A more accurate detection of knot properties in CT scanning will improve the quality prediction and contribute to a more efficient use of the wood resource. The knot properties are related to the crown development and branching of the trees and can be predicted from external tree measurements and forest inventory data. The project aims at improving the algorithms for knot detection in CT scanning by use of artificial intelligence (AI), combining data from CT scanning with physical measurements of knots and forest inventory data, and developing new models for estimating knot properties and strength and stiffness of sawn timber.

Knot-extraction methodology from CT log scans will be developed with more details and a higher accuracy than those of existing algorithms. This will correspond to a significant improvement over existing methods. Industrial CT scanning of logs is becoming a more widespread tool in sawmill production and has proven to considerably increase the volume of high-value sawn timber. Still, there is great potential in improving its accuracy. The novel image analysis methods based on deep learning and neural network approaches, in combination with improved models, will enable a more precise production optimisation from forestry to log and final end-product (sawn-timber). The project is divided into 9 WPs covering different levels of the processing chain, centering the branch/knot structure, and linking tree (crown, growth) and mechanical wood (strength, stiffness) properties. A unique database will be created and will be used for the following purposes: - AI-based algorithms for better extraction of knots from CT images, combined with statistical modelling based on the training data sets. - Combination of forest inventory data and detailed tree growth data, external stem, crown and branch measurements and knot properties with data from CT scanning to improve sawing simulations. It will also make systems for more optimal bucking of stems possible, and feedback to forestry and silviculture, and therefore the forest value chain will be more sustainable due to a higher material efficiency. - Systematically compare CT measurements of stem/knot structure in green and dry conditions with manual, physical measurements of the same properties in order to improve algorithms for knot extraction from CT images. - Development of new grading models for estimating strength and stiffness of virtual boards based on CT data.