Automatic identification of charcoal origin based on deep learning - Rodrigues de Oliveira Neto, Ricardo; Ferreira Rodrigues, Larissa; Mari, João Fernando; Coelho Naldi, Murilo; Gomes Milagres, Emerson; Rocha Vital, Benedito; Oliveira Carneiro, Angélica de Cássia; Breda Binoti, Daniel Henrique; Falco Lopes, Pablo; Garcia Leite, Helio (Maderas. Ciencia y Tecnología, 2021)
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.