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Wood identification based on longitudinal section images by using deep learning


Automatic species identification has the potential to improve the efficacy and automation of wood processing systems significantly. Recent advances in deep learning allowed for the automation of many previously difficult tasks, and in this paper, we investigate the feasibility of using deep convolutional neural networks (CNNs) for hardwood lumber identification. In particular, two highly effective CNNs (ResNet-50 and DenseNet-121) as well as lightweight MobileNet-V2 were tested. Overall, 98.2% accuracy was achieved for 11 common hardwood species classification tasks.

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  1. Alfonso V, Baas P, Carlquist S, Chimelo J, Coradin V, Détienne P, Gasson P, Grosser D, Ilic J, Kuroda K, Miller R, Ogata K, Richter H, Welle B, Wheeler E (1989) Iawa list of microscopic features for hardwood identification: with an appendix on non-anatomical information. IAWA J 10:221–358.

    Article  Google Scholar 

  2. Gazo R, Wells L, Krs V, Benes B (2018) Validation of automated hardwood lumber grading system. Comput Electron Agric 155:496–500.

    Article  Google Scholar 

  3. Geirhos R, Rubisch P, Michaelis C, Bethge M, Wichmann FA, Brendel W (2019) IImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: Proc. 6th Int. Conf. Learn. Represent.

  4. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press, Cambridge

    Google Scholar 

  5. Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern Syst SMC 3(6):610–621.

    Article  Google Scholar 

  6. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp 770–778.

  7. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp 4700–4708

  8. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proc. 32nd Int. Conf. Mach. Learn., pp 448–456

  9. Kingma DP, Ba J (2015) Adam: A method for stochastic optimization. In: Proc. 3rd Int. Conf. Learn. Represent

  10. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324.

    Article  Google Scholar 

  11. Martins J, Oliveira L, Nisgoski S, Sabourin R (2013) A database for automatic classification of forest species. Mach Vis Appl 24(3):567–578.

    Article  Google Scholar 

  12. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29(1):51–59.

    Article  Google Scholar 

  13. Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–609.

    CAS  Article  PubMed  Google Scholar 

  14. Paula Filho PL, Oliveira LS, Nisgoski S, Britto AS (2014) Forest species recognition using macroscopic images. Mach Vis Appl 25(4):1019–1031.

    Article  Google Scholar 

  15. Ravindran P, Costa A, Soares R, Wiedenhoeft AC (2018) Classification of cites-listed and other neotropical meliaceae wood images using convolutional neural networks. Plant methods 14(1):25

    Article  Google Scholar 

  16. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp 4510–4520.

  17. Settle J, Gonso C (2020) 2020 Indiana forest products price report and trend analysis. Tech. rep, Indiana Department of Natural Resources.

  18. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Technol 15(1):1929–1958

    Google Scholar 

  19. Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Series B Stat Methodol 36(2):111–133

    Google Scholar 

  20. Wells L, Gazo R, Del Re R, Krs V, Benes B (2018) Defect detection performance of automated hardwood lumber grading system. Comput Electron Agric 155:487–495.

    Article  Google Scholar 

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This research was supported by the Foundation for Food and Agriculture Research Grant ID: 602757 to Benes and McIntire Stennis grant accession no. 1012928 to Gazo from the USDA National Institute of Food and Agriculture. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the respective funding agencies.

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Correspondence to Bedrich Benes.

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Wu, F., Gazo, R., Haviarova, E. et al. Wood identification based on longitudinal section images by using deep learning. Wood Sci Technol 55, 553–563 (2021).

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