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Automatic identification of charcoal origin based on deep learning

Author
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

Full text
https://revistas.ubiobio.cl/index.php/MCT/article/view/4921
10.4067/s0718-221x2021000100465
Abstract
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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Discipline
Artes, Arquitectura y UrbanismoCiencias Agrarias, Forestales y VeterinariasCiencias Exactas y NaturalesCiencias SocialesDerechoEconomía y AdministraciónFilosofía y HumanidadesIngenieríaMedicinaMultidisciplinarias
Institutions
Universidad de ChileUniversidad Católica de ChileUniversidad de Santiago de ChileUniversidad de ConcepciónUniversidad Austral de ChileUniversidad Católica de ValparaísoUniversidad del Bio BioUniversidad de ValparaísoUniversidad Católica del Nortemore

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