Semi-supervised learning for quality control of high-value wood products

The quality control of wood products is often only checked at the end of the production process so that countermeasures can only be taken with a time delay in the event of fluctuations in product quality. This often leads to unnecessary and cost-intensive rejects. Furthermore, since quality control often requires additional procedural steps to be performed by a skilled worker, testing is time-consuming and costly. While traditional machine learning (ML) methods based on supervised learning have been used in the field with some success, the limited availability of labeled data is the major hurdle for further improving model performance. In the present study, the potential of enhancing the performance of the ML methods random forest (RF) and support vector machines (SVM) for quality classification by using semi-supervised learning (SSL) was investigated. Labeled and unlabeled data were provided by Swiss Wood Solutions AG, which produces densified wood for high-value wood products such as musical instruments. The developed approach includes labeling of the unlabeled data using SSL, training and 10k cross-validation of the ML algorithms RF and SVM, and determining the generalization ability using the hold-out test set. Based on the evaluation indices such as accuracy, F1-score, recall, false-positive-rate and confusion matrices, it was shown that SSL could enhance the prediction performance of the quality classification of ML models compared to the conventional supervised learning method. Despite having a small dataset, the work paves the way for future applications of SSL for wood quality assessment.