Stem shapes and wood properties are typically unknown at the time of harvesting. To date, approaches that integrate information about past tree growth into the harvesting and bucking process are rarely used. New models were developed and their potential demonstrated for stem bucking procedures for cut-to-length harvesters that integrate information about external and internal stem characteristics detected during harvesting. In total 221 stems were sampled from nine Scots pine (Pinus sylvestris L.) stands in Finland. The widths of rings 11−20 from the pith were measured using images taken from the end face of each butt log. The total volume of knots in each whorl was measured by using a 4D X-ray log scanner. In addition, 13 stems were test sawn, and the diameters of individual knots were measured from the sawn boards. A model system was developed for predicting the horizontal diameter of the thickest knot for each whorl along a stem. The first submodel predicts the knot volume profile from the stem base upwards, and the second submodel converts the predicted knot volume to maximum knot diameter. The results showed that the knottiness of stems of a given size may vary greatly depending on their early growth rate. The developed system will be used to guide logging operations to achieve more profitable bucking procedures.

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In cut-to-length logging, the harvester operator adjusts the bucking in accordance with visible defects on processed stems. Some of the defects, such as a sweep on the bottom of the stem, decrease the yield and quality of sawn products and are difficult for the operator to notice. Detecting the defects with improved sensors would support the operator in his qualitative decision-making and increase value recovery of logging. Predicting the maximum bow height of the bottom log in Norway spruce (Picea abies (L.) Karst.) with log end face image and stem taper was investigated with two modelling approaches. A total of 101 stems were selected from five clear-cut stands in southern Finland. The stems were crosscut and taper measured, and the butt ends of the bottom logs were photographed. The stem diameter, out-of-roundness, and pith eccentricity were measured from the images while the max. bow height was measured by a 3D log scanner at a sawmill. The bottom logs with an eccentric pith had higher max. bow height. In addition, a highly conical bottom part of the stem was more common on the bottom logs with a large max. bow height. Applying both log end face image and stem taper measurements gave the best model fit and detection accuracy (76%) for bottom logs with a large max. bow height. The results indicate that the log end face image and stem taper measurements can be utilised to aid harvester operator in deciding an optimised length for logs according to the bow height.