We use data produced by industrial wood grading machines to train a machine learning model for predicting strength-related properties of wood lamellae from colour images of their surfaces. The focus was on samples of Norway spruce (Picea abies) wood, which display visible fibre pattern formations on their surfaces. We used a pre-trained machine learning model based on the residual network ResNet50 that we trained with over 15 000 high-definition images labelled with the indicating properties measured by the grading machine. With the help of augmentation techniques, we were able to achieve a coefficient of determination (R²) value of just over 0.9. Considering the ever-increasing demand for construction-grade wood, we argue that computer vision should be considered a viable option for the automatic sorting and grading of wood lamellae in the future.

我们使用工业木材分级机产生的数据来训练机器学习模型，用于根据木片表面的彩色图像预测与强度相关的属性。重点是挪威云杉（Picea abies）木材的样品，其表面显示出可见的纤维图案结构。我们使用基于残差网络 ResNet50 的预训练机器学习模型，该模型使用超过 15,000 张高清图像进行训练，这些图像标有分级机测量的指示属性。借助增强技术，我们能够实现略高于 0.9 的决定系数 ( R ² ) 值。考虑到对建筑级木材的需求不断增长，我们认为计算机视觉应该被视为未来自动分类和分级木片的可行选择。