Predicting internal yellow-poplar log defect features using surface indicators

Determining the defects that are located within the log is crucial to understanding the tree/log resource for efficient processing. However, existing means of doing this non-destructively requires the use of expensive X-ray/CT, MRI, or microwave technology. These methods do not lend themselves to fast, efficient, and cost-effective analysis of logs and tree stems in the mill. This study quantified the relationship between external defect indicators and internal defect characteristics for yellow-poplar logs. A series of models were developed to predict internal features using visible external features, log diameter, indicator width, length, and rise. Good correlations and small prediction errors were observed with sound (sawn), overgrown, and unsound knot defects. For less severe defects such as adventitious buds/clusters and distortion type defects weaker correlations were observed, but the magnitude of prediction errors was small and acceptable.