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
Highlights
Brazil is one of the largest charcoal producers, with a reaching 5,3 million tons in 2019 (Ministry of Mines and Energy 2020)
We study an efficient method for automatic identification of charcoal origin based on deep learning and cross-validation k-fold technique using macroscopic images
In order to assess the values of True Positive Rate (TPR) against the False Positive Rate (FPR) we analyzed the Receiver Operating Characteristic (ROC) (AUC) for each iteration of the k-fold
Summary
Brazil is one of the largest charcoal producers, with a reaching 5,3 million tons in 2019 (Ministry of Mines and Energy 2020). Besides being a world producer, Brazil is one of the largest consumers of charcoal. Most of this production is destined for the internal market, mainly for the pig-iron and steel sectors and lesser, for the ferroalloy sector and residential consumption (ABRAF 2013). This demand is not supplied through charcoal using planted forests, making the illegal exploitation of native forests attractive.
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