Validation of a CT knot detection algorithm on fresh Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) logs

Abstract

& Key message A fully automated algorithm allowed knot detection and positioning on computed tomography (CT) images of Douglas-fir logs. The detection of knot diameter and status could benefit from further improvements, i.e., testing other configurations and implementing texture measures. Manual measurement on CT images allows for tridimensional assessment and greater attainable sampling, while manual measurement on discs provides additional color and texture information. & Context Computed tomography (CT) is a very successful tool to non-destructively acquire the internal knot structure of a log. To enable large-scale applications, an algorithm that automatically detects knots is required. The accuracy of such algorithms depends heavily on the species and image resolution. & Aim This study validates a knot detection algorithm (Johansson et al. in Comput Electron Agric 96:238-45, 2013) on fresh Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) logs. & Methods In this study, 282 knots were sampled from 15 logs, selected from six 78-year-old trees in southwest Germany. The validation of the algorithm's knot detection was performed via comparison against two manual methods: on physical samples and on CT images. & Results The saturated sapwood negatively influences the overall knot detection, which causes underestimation of knot diameter in this area or incomplete detection. The algorithm tended to overestimate knot diameter, longitudinal position, and knot length. & Conclusion The algorithm provides the knot position with satisfactory accuracy. Other settings on contrast and considered volume around a knot can be tested within the algorithm, as well as new development and implementation of texture measures in the image analysis to improve the accuracy results for Douglas-fir in future investigations.

Predicting Douglas-fir knot size in the stand: a random forest model based on CT and field measurements

“…The algorithm has a variety of settings that can be adjusted according to the log species, moisture condition, size, etc. Different configurations of the knot detection algorithm for Douglas-fir were tested by Longo et al (2019), and the most suitable configuration was the one applied in this study.…”

“…2). Knot diameter was calculated at intervals of 20 mm along the log radius, from pith to bark (Longo et al 2019). ø = knot diameter correspondent arc, as given by the knot detection algorithm output (in rad).…”

“…The importance of the knot status (SBW) for BD max , as it shows the prominent position in the importance ranking analysis, can be explained by the different diameter development of sound and dead branches. Although the CT knot detection has limitations (Longo et al 2019), sound knots have not reached their diameter peak internally yet in comparison to dead knots, which show a clear BD max within the log.…”

“…In contrast, the present study included both live and dead branches, BD max consisted of an internal measurement, and a different branch diameter measurement technique was applied (CT scanning). In particular, the combination of dead knots and the CT method add variance to M BDmax , due to detection accuracy (Longo et al 2019).…”

“…These may not always be precise, as reported by Johansson et al (2013) for Norway spruce (Picea abies [L.] Karst.) and Scots pine (Pinus sylvestris L.), by for jack pine and white spruce (Picea glauca [Moench] Voss), and by Longo et al (2019) for Douglas-fir. Therefore, there are opportunities for future improvements in the presented models, as more advanced CT image resolution might provide even higher accuracy, and/or the applied algorithm may undergo modifications to enhance the knot boundary detection, improving the knot size extraction.…”