Compression wood is a feature in softwoods that is undesired in sawn wood p
roducts due to its tendency to bend and crook as the moisture content chang
es. An automatic compression-wood detection method was developed and tested
on southern yellow pine lumber in the green condition. Sixteen lumber spec
imens were scanned using both a color camera and an X-ray scanner. Color in
formation was shown to have significant and consistent differences between
compression wood and clear wood. However, X-ray information was found to co
ntain large density variations in green lumber due to inconsistent moisture
content that would mask density variations arising from compression wood.
Therefore, it was concluded that X-ray information would not be useful in d
etecting compression wood in green southern yellow pine lumber. A multivari
ate regression model was developed based only on color information from one
of the board samples. A nonlinear prediction model was produced by using t
he original color image data and expanded variables derived from the color
images. The model based on one board sample was then applied on all boards.
Classified images of the board surfaces were produced and compared to manu
ally detected compression wood. An overall accuracy of 87% was observed in
the classification of compression wood.