Journal of the Korean Wood Science and Technology

The Korean Society of Wood Science & Technology (한국목재공학회)
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Wood Species Classification Utilizing Ensembles of Convolutional Neural Networks Established by Near-Infrared Spectra and Images Acquired from Korean Softwood Lumber

  • Yang, Sang-Yun (Department of Forest Sciences, Seoul National University) ;
  • Lee, Hyung Gu (National Instrumentation Center for Environmental Management (NICEM), Seoul National University) ;
  • Park, Yonggun (Department of Forest Sciences, Seoul National University) ;
  • Chung, Hyunwoo (Department of Forest Sciences, Seoul National University) ;
  • Kim, Hyunbin (Department of Forest Sciences, Seoul National University) ;
  • Park, Se-Yeong (Department of Forest Biomaterials Engineering, Kangwon National University) ;
  • Choi, In-Gyu (Department of Forest Sciences, Seoul National University) ;
  • Kwon, Ohkyung (National Instrumentation Center for Environmental Management (NICEM), Seoul National University) ;
  • Yeo, Hwanmyeong (Department of Forest Sciences, Seoul National University)
  • Received : 2019.05.01
  • Accepted : 2019.07.02
  • Published : 2019.07.25
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Abstract

In our previous study, we investigated the use of ensemble models based on LeNet and MiniVGGNet to classify the images of transverse and longitudinal surfaces of five Korean softwoods (cedar, cypress, Korean pine, Korean red pine, and larch). It had accomplished an average F1 score of more than 98%; the classification performance of the longitudinal surface image was still less than that of the transverse surface image. In this study, ensemble methods of two different convolutional neural network models (LeNet3 for smartphone camera images and NIRNet for NIR spectra) were applied to lumber species classification. Experimentally, the best classification performance was obtained by the averaging ensemble method of LeNet3 and NIRNet. The average F1 scores of the individual LeNet3 model and the individual NIRNet model were 91.98% and 85.94%, respectively. By the averaging ensemble method of LeNet3 and NIRNet, an average F1 score was increased to 95.31%.

Keywords

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Fig. 1. Confusion matrix (Yang et al., 2019).

Table 1. Sample class index and number of samples of each fold in k-fold cross-validation (k = 5)

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Table 2. Architecture of LeNet3 and NIRNet models

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Table 3. Performance measures of LeNet3 model by k-fold cross-validation (k = 5)

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Table 4. Performance measures of NIRNet model by k-fold cross-validation (k = 5)

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Table 5. Performance measures of LeNet3-NIRNet ensemble model by averaging method.

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Table 6. Performance measures of LeNet3-NIRNet ensemble model by max-voting method.

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