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The increased pressure on timber supply due to a reduced forest land base urges the development of new approaches to fully capture the value of forest products. This paper investigates the effects of knowing the position of knots on lumber volume, value, and grade recoveries in curve sawing of 31 white spruce (Picea glauca (Moench) Voss) and 22 jack pine (Pinus banksiana Lamb.) trees. Internal knot position was evidenced by X-ray computed tomography (CT) imaging, followed by the application of a knot-detection algorithm allowing log reconstruction for use as input in the Optitek sawing simulation software. Comparisons of the three levels of sawing optimization (sweep up, shape optimized, and knot optimized) revealed that considering internal knots before log sawing (e.g., knot optimized) generated 23% more lumber value for jack pine and 15% more for white spruce compared with the traditional sweep-up sawing strategy. In terms of lumber quality, the knot-optimized strategy produced 38% more pieces of grade No. 2 and better in jack pine and 15% more such pieces in white spruce compared with the sweep-up strategy. These results indicate a great potential to increase manufacturing efficiency and profitability by implementing the CT scanning technology, which should aid in developing a strong bioeconomy based on an optimized use of wood. [ABSTRACT FROM AUTHOR]

X-ray computed tomography (CT) of logs means possibilities for optimizing breakdown in sawmills. This depends on accurate detection of knots to assess internal quality. However, as logs are stored, they dry to some extent, and this drying affects the density variation in the log and therefore the X-ray images. For this reason, it is hypothetically difficult to detect log features in partially dried logs using X-ray CT. This paper investigates the effect of improper heartwood-sapwood border detection, possibly due to partial drying, on knot detection in jack pine ( Pinus banksiana Lamb.) and white spruce ( Picea glauca (Moench) Voss) logs from New Brunswick, Canada. An automatic knot detection algorithm was compared to manual reference knot measurements, and the results showed that knot detection was affected by detected heartwood shape. It was also shown that logs can be sorted into two groups based on how well the heartwood-sapwood border is detected to separate logs with a high knot detection rate from those with a low detection rate. In that way, a decision can be made whether or not to trust the knot models obtained from CT scanning. This can potentially aid both sawmills and researchers working with log models based on CT. [ABSTRACT FROM AUTHOR]