Automatic identification of charcoal origin based on deep learning


  • Ricardo Rodrigues de Oliveira Neto
  • Larissa Ferreira Rodrigues
  • João Fernando Mari
  • Murilo Coelho Naldi
  • Emerson Gomes Milagres
  • Benedito Rocha Vital
  • Angélica de Cássia Oliveira Carneiro
  • Daniel Henrique Breda Binoti
  • Pablo Falco Lopes
  • Helio Garcia Leite



Charcoal, classification, deep learning, native wood, preprocessing


The differentiation between the charcoal produced from (Eucalyptus) plantations and native forests is essential to control, commercialization, and supervision of its production in Brazil. The main contribution of this study is to identify the charcoal origin using macroscopic images and Deep Learning Algorithm. We applied a Convolutional Neural Network (CNN) using VGG-16 architecture, with preprocessing based on contrast enhancement and data augmentation with rotation over the training set images. on the performance of the CNN with fine-tuning using 360 macroscopic charcoal images from the plantation and native forests. The results pointed out that our method provides new perspectives to identify the charcoal origin, achieving results upper 95 % of mean accuracy to classify charcoal from native forests for all compared preprocessing strategies.


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How to Cite

Rodrigues de Oliveira Neto, R. ., Ferreira Rodrigues, L. ., Mari, J. F. ., Coelho Naldi, M. ., Gomes Milagres, E. ., Rocha Vital, B. ., Oliveira Carneiro, A. de C. ., Breda Binoti, D. H. ., Falco Lopes, P. ., & Garcia Leite, H. . (2021). Automatic identification of charcoal origin based on deep learning. Maderas-Cienc Tecnol, 23, 1–12.