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

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.