A multivariate approach to automatic grading of Pinus sylvestris sawn timber

Abstract

The objective of this study was to create an easier way to handle the often complicated and intricate situations with which the operator of an automatic grading system is faced each time a change to the grading rules is proposed. The scope of the study was the possibility of a holistic method of automatic appearance grading of sawn wood similar to manual grading and based on multivariate statistics. The study was based on 90 Scots pine (Pinus sylvestris L.) sawlogs. The logs were sawn and the boards were scanned and manually graded. The result of the manual grading was defined as the true grade. Models for prediction of board grade based on aggregated defect variables were calibrated using partial least squares regression. The classification based on the multivariate models resulted in 80–85% of the boards being correctly graded according to the manual grading. In conclusion, this paper shows that a multivariate statistical approach for grading timber is a possible way to simplify the process of grading and to customize the grading rules when using an automatic grading system.