Identification of Peruvian timber species based on image processing and convolutional neural networks

Abstract

The identification of timber species is highly important worldwide due to their global trade and the arising requirement of controlling, regulating and monitoring illegal logging. Peru has an important timber trade due to extensive forest resources in the Peruvian Amazon. This context makes it necessary to have technological tools for timber species identification using tree leaves, in an attempt to control deforestation in the Amazon forests. Current identification methods require manual labor, are subjective and inefficient regarding time, cost and precision. Moreover, the scientific literature does not include electronic devices nor algorithms which exclusively identify Peruvian timber species. With this in mind, this work proposes a portable electronic equipment that detects 11 timber species from the Peruvian jungle, based on image processing techniques and convolutional neural networks (CNN). The device consists of a carbon fiber structure, a keyboard, a 7-inch display, internal lighting and a tray for the leaf sample. The algorithm uses color filters, thresholding and other operations to segment the leaf and then input it to a neural network. After comparing the AlexNet, VGG-16 and MobileNet architectures, the last one shows the best performance, with an average precision of 98.64% when identifying the 11 timber species.