Comparison of classification performance by the applied conditionClassification performance of oak species using CNNFigure 1 shows the results of comparing the classification accuracy and loss in the test phase of the CNN architecture using the whole-part and earlywood-part datasets of the six oak woods. As the number of epochs increased in both dataset conditions, the loss decreased and the accuracy increased. This trend appeared in the process of updating weights and biases repeatedly with increasing epochs of the CNN49, indicating proper performance in learning and classification through the CNN.Figure 1Verification results of CNN architecture using the whole-part and earlywood-part datasets of the six oak woods. (a) loss in the whole-part dataset condition. (b) loss in the earlywood-part dataset condition. (c) accuracy in the whole-part dataset condition. (d) accuracy in the earlywood-part dataset condition.In the verification results of the whole-part dataset, the classification accuracy and loss under the conditions of the Adam and RMSProp optimizers rapidly stabilized within the range of 10–20 epochs, whereas the conditions of SGD were relatively gently stabilized. The learning speed according to the type of optimizer generally depends on the difference in the operational principle50,51. The conditions trained by the augmented dataset tended to stabilize in the range of 80–100 epochs regardless of the optimizer. In particular, the SGD non-augmented dataset condition showed the gentlest stabilization, with a slope close to linear. In the testing phase, the augmented dataset tended to stabilize relatively quickly in the range of 20–40 epochs compared to the non-augmented dataset for all optimizer conditions, whereas the Adam and RMSProp conditions using the non-augmented dataset showed a pattern of overfitting after 20–40 epochs. In particular, the classification accuracy at the final stage in the validation condition using the augmented dataset was nearly 20% higher than that using the non-augmented dataset, which was due to the improvement in the model’s generalization performance owing to the increase in the diversity of the dataset52.Whereas, in the verification results of the earlywood-part dataset, the fluctuation trend of classification accuracy and loss was more clearly observed than that in the whole-part dataset. The classification accuracy and loss were observed to stabilize at approximately 40 epochs, regardless of the optimizer and dataset augmentation. However, when the augmented dataset was applied, the loss was lower, and the classification accuracy was higher than when the non-augmented dataset was applied. Meanwhile, the difference in classification accuracy and loss between the augmented and non-augmented dataset conditions decreased significantly compared with the validation condition using the whole-part dataset. This implies that even when using the non-augmented dataset, similar levels of performance to the augmented dataset can be achieved in the earlywood dataset for learning, and it can be interpreted that the convolutional layer extracts various features in earlywood, achieving excellent generalization performance even with a small dataset52.Anatomical factors affecting wood species classification performanceGrad-CAM analysis of whole-part datasetTable 3 shows the weights of the parts recognized as classification indicators using the Grad-CAM technique, which was applied to classify the six oak species based on earlywood and latewood cross-sectional images. As a result of the analysis of the factors affecting species classification in the Grad-CAM technique using the cross-sections of the oak species, the earlywood vessels and well-developed broad rays over 10 seriates were identified as common classification indicators in most species. The arrangement of earlywood vessels in the earlywood of Q. acutissima acted as a factor influencing the classification, whereas the area composed of only fibers without broad-ray tissue and axial parenchyma cells was involved in the classification of Q. aliena. The arrangement of earlywood vessels and distribution of axial parenchyma cells around the latewood affected the classification of Q. dentata. The fiber area without axial parenchyma cells and broad rays in the cross-section was identified as a classification indicator for Q. mongolica. The arrangement of earlywood vessels, axial parenchyma cells, and broad rays were identified as classification factors for Q. serrata. Axial parenchymal cells adjacent to vessels around the broad rays did not affect the classification of Q. serrata. Most traits such as the arrangement of vessels, axial parenchyma cells, and fibers were confirmed as classification indicators in Q. variabilis. However, the parenchymal cells distributed around the vessels did not affect the classification.
Table 3 Analysis of the classification factors of six oak species using the whole-part micrographs.In the whole part dataset, species classification based on convolutional neural network was affected by the arrangement of pores, broad rays, and axial parenchyma cells.Table 4 lists the weights of the parts recognized as classification indicators in the Grad-CAM technique using the earlywood dataset. Compared with the whole-part images, the arrangement of earlywood vessels, which is a major characteristic, was more clearly observed, and classification indicators, such as wood fiber and axial parenchyma, were found around the earlywood vessels. Oak species undergo rapid growth from spring to summer owing to seasonal factors53, leading to the significant development of earlywood in the xylem, which contributes prominently to the classification of the species; thus, it is regarded as a determining factor for classification among the oak species in this study. Tyloses in the earlywood vessels and the axial parenchyma cells around the earlywood vessels were also classified as indicators of Q. acutissima. The classification accuracy of Q. aliena was affected by earlywood vessels, tyloses in earlywood vessels, and axial parenchyma cells around latewood vessels. Tyloses in earlywood vessels and axial parenchyma cells around the earlywood vessels were also classification indicators of Q. dentata. Q. mongolica is affected by its overall structural components, such as the arrangement of earlywood vessels, fibers, and axial parenchyma cells. Q. serrata was characterized by a lower occurrence rate of tyloses in earlywood vessels than in other species and did not affect the classification of axial parenchyma cells. Although the arrangement of earlywood vessels was confirmed as a classification indicator in Q. variabilis, the axial parenchymal cells around the earlywood vessels were excluded from the classification indicators.
Table 4 Analysis of the classification factors of six oak species using the earlywood micrographs.The results suggested that species classification based on the convolutional neural network using an earlywood dataset was affected by the arrangement of the pores, broad rays, and axial parenchyma cells.Statistical analysisCorrelation among the factorsTable 5 presents the correlations between the variables applied to the test process of the CNN architectures. The loss tended to decrease with an increasing number of epochs in whole epochs verification, whereas the accuracy tended to increase in proportion to the number of epochs.
Table 5 Correlation of the factors influencing classification performance.Among the optimizers in whole epochs condition, Adam (0.127**) and SGD (− 0.160**) had the highest and lowest impact on classification accuracy, respectively, whereas RMSProp did not show a significant difference in classification accuracy. Dataset augmentation showed a relatively higher impact (0.351**) on classification accuracy than the other factors. In contrast, the accuracy tended to decrease with the application of the whole part or non-augmented dataset, whereas it increased with the application of the earlywood part or augmented dataset. The loss tended to be opposite to that of accuracy.The factors affecting classification accuracy, such as epochs, optimizer, and dataset composition (whole-part, earlywood), disappeared, and the impact of dataset augmentation increased more than twice from 0.351** to 0.747**. The increase in impact is expected to be attributed to the variation in classification accuracy and loss minimization after reaching the convergence point. Dataset augmentation could be a major factor affecting classification performance.Homogeneous subsetTable 6 presents the results for the homogeneous subsets among the conditions of the dataset based on the verification results shown in Fig. 1.
Table 6 Comparison of average loss and accuracy among optimizers.In the results of whole-epochs verification, losses were classified into multiple subsets. The first identified subset included conditions such as whole-part-SGD, earlywood-Adam, and earlywood-RMSProp, which utilized an augmented dataset. The second subset included four conditions: two conditions for the augmented dataset, whole-part-RMSProp and earlywood-SGD, and two conditions for the non-augmented dataset, earlywood-SGD and earlywood-RMSProp. In the third subset, three conditions were classified: earlywood-SGD and earlywood-Adam for the non-augmented dataset and earlywood-SGD for the augmented dataset. The fourth subset was identified according to three conditions: whole-part-Adam and earlywood-Adam for the non-augmented dataset and earlywood-SGD for the augmented dataset. The fifth subset identified three conditions: whole-part-SGD, whole-part-Adam, and earlywood-Adam, which utilized a non-augmented dataset.Classification accuracy was divided into two major subsets. The first homogeneous subset consisted of five of the six earlywood dataset conditions, excluding the ADAM-augmented condition. The second homogeneous subset also consisted of five conditions: whole-part-SGD, earlywood-SGD, earlywood-Adam, and earlywood-RMSProp conditions that utilized an augmented dataset, and the earlywood-RMSProp condition that utilized a non-augmented dataset. Based on these results, it was concluded that applying the earlywood dataset produced similar results without a significant impact on the conditions.In the results of last five epochs verification, most conditions during the test phase had a classification accuracy of around 70%. Only some conditions, such as whole-part-SGD, whole-part-Adam, whole-part-RMSProp, and earlywood-SGD that applied an augmented dataset, had a classification accuracy of over 80%. The condition with the highest classification accuracy among the conditions was whole-part-Adam that applied an augmented dataset, showing an accuracy of approximately 85.7%. However, there was no significant difference in classification accuracy between the conditions that produced a classification accuracy of over 80% mentioned earlier and the whole-part-Adam condition.Table 7 presents the results of the homogeneous subset analysis between the indicators using the average accuracy in the final five epochs of the test phase, as shown in Table 6. Data augmentation directly affected the classification accuracy, and the classification accuracy before and after augmentation was verified as a significantly independent subset. There was no significant difference in classification accuracy between the whole-part and earlywood datasets. The classification accuracy according to the optimizers SGD, Adam, and RMSProp was classified as a homogeneous subset with no difference.
Table 7 Comparison of average accuracy in the final five epochs per factor in the test phase.
Performance of convolutional neural network (CNN) and performance influencing factors for wood species classification of Lepidobalanus growing in Korea
