Automatic identification of charcoal origin based on deep learning

• Ricardo Rodrigues de Oliveira Neto ^[1] ; Larissa Ferreira Rodrigues ^[1] ; João Fernando Mari ^[1] ; Murilo Coelho Naldi ^[2] ; Emerson Gomes Milagres ^[1] ; Benedito Rocha Vital ^[1] ; Angélica de Cássia Oliveira Carneiro ^[1] ; Daniel Henrique Breda Binoti ^[3] ; Pablo Falco Lopes ^[3] ; Helio Garcia Leite ^[1]
  1. [1] Universidade Federal de Viçosa

     Universidade Federal de Viçosa

     Brasil

     
  2. [2] Universidade Federal de São Carlos

     Universidade Federal de São Carlos

     Brasil

     
  3. [3] DAP Florestal, Centro Empresarial da Serra. Parque Res. de Laranjeiras, Serra, ES, Brazil

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• Localización: Maderas: Ciencia y tecnología, ISSN 0717-3644, ISSN-e 0718-221X, Vol. 23, Nº. 0, 2021
• Idioma: inglés
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• Resumen

  • 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