Deep Learning Applied to Identification of Commercial Timber Species from Peru

Automatic identification of timber species is a necessity and a challenge in several aspects, especially for government institutions in charge of monitoring forestry resources. In this paper, we propose a methodology to develop an efficient computational model to identify wood samples of seven commercial timber species chosen according to availability of samples properly classified by specialists. For this, we created image sets of wood of seven timber species using a portable digital microscope connected to a personal computer. These images were divided into patches and grouped into training, validation and test sets, with which a convolutional neuronal network was trained. It consist of four layers: two convolutional layers with max pooling and two fully connected layers at the output. Previously, three image patch sizes were evaluated to find the highest accuracy value, precision and sensitivity for the identification. The results show a good performance of the computational model with an accuracy of 94.05% and precision and sensitivity values around 90%, under proposed conditions.