## 5 Conclusions  
This paper describes the crack severity level classification of square timber cross-sectional images using a CNN. Thirty-two cedar timbers of 3000 mm in length were dried at a high temperature. Cross-sectional images of two different parts of each timber were obtained. Consequently, 64 cross-sectional images were acquired in total. The images were classified into three classes, i.e., A (Good), B (Neutral), and C (Bad), based on the severity of the cracks. We subsequently investigated the automatic classification of images using the ResNet50-CNN. Because the image data were insufficient, flipping and rotation were applied while training the CNN to virtually augment the image data.  
In addition, repeated random subsampling validation was performed to evaluate the CNN using a small amount of data. Three images from each class were randomly selected for testing, and the remaining images were used to train the CNN. The loss decreased as the epochs progressed. The classification accuracy was evaluated using test data that were not used for the training; the aforementioned procedure was performed 20 times. In the 20 trials, the average classification accuracy for the test data was 86.67%. We analyzed the misjudged images and observed confusion similar to human evaluators. Therefore, we confirmed that the CNN has the potential to evaluate cross-sectional cracks on behalf of humans. However, the light condition to capture images was constant in this paper. In the future, it will be necessary to prepare more image data to evaluate the performance of the CNN. In addition, we will need to construct a system in the field that enables the evaluation of cracks under various light conditions.  
This study could decrease the burden of quality control on vendors, as well as researchers improving high-temperature drying techniques, who have to rank many square timbers visually. The practical application of this study is to replace humans with artificial intelligence that automatically evaluates the quality of timbers. A skilled human evaluator could comprehensively evaluate timber quality, taking into account not only the cracks number, area, and length, but additionally the shape and location of the cracks in the cross-section. CNNs have the potential to perform the detailed judgment of a skilled evaluator if trained with various image data.  
S.K.: conceptualization, methodology, software, validation, formal analysis, investigation, writing original draft preparation, writing review and editing, and visualization; N.W.: conceptualization, methodology, validation, formal analysis, investigation, supervision, and project administration; K.S.: conceptualization, methodology, validation, formal analysis, and investigation; T.T.: conceptualization, methodology, validation, formal analysis, and investigation; T.K.: validation, formal analysis, and investigation; and investigation; R.T.: methodology, software, validation, formal analysis, and investigation; H.N.: conceptualization, methodology, validation, formal analysis, and investigation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by JSPS KAKENHI (grant number JP20K06116).  
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The authors would like to thank [PERSON] and [PERSON] of MathWorks for their technical advice.  
The authors declare no conflict of interest.