We used principal component analysis(PCA) and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as a classifier, improves identification accuracy. We extracted 25 features, including geometry and regional features, gray-scale texture features, and invariant moment features, from wood board images and then integrated them using PCA, and selected eight principal components to express defects. After the fusion process, we used the features to construct a data dictionary, and realized the classification of defects by computing the optimal solution of the data dictionary in l_1 norm using the least square method. We tested 50 Xylosma samples of live knots, dead knots, and cracks. The average detection time with PCA feature fusion and without were 0.2015 and 0.7125 ms,respectively. The original detection accuracy by SOM neural network was 87 %, but after compressed sensing, it was 92 %. We used principal component analysis (PCA) and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as a classifier, improves identification accuracy. geometry and regional features, gray-scale texture features, and invariant moment features, from wood board images and then integrated them using PCA, and selected eight principal components to express defects. After the fusion process, we used the features to construct a data dictionary , and realized the classification of defects by computing the optimal solution of the data dictionary in l_1 norm using the least square method. We tested 50 Xylosma samples of live knots, dead knots, and cracks. The average detection time with PCA feature fusion and without were 0.2015 and 0.7125 ms, respectively. The original detection accuracy by SOM neural network was 87%, but after compressed sensing, it was 92%.