Evaluating the Significance of Cutting Planes of Wood Samples When Training CNNS for Forest Species Identification

Abstract

With the goal of quantifying the importance of each of the cutting planes of wood samples in the training process of a convolutional neural network that identifies forest species based on images of those cutting planes, we propose a convolutional model that is trained from scratch with images of transverse, radial, and tangential sections of Costa Rican forest species wood samples. The best Top1-accuracy achieved is 89.58% when the network is trained with transverse sections only. Because this is more than 20% better than the accuracy achieved when using any of the other two sections individually, we conclude that this is the most significant section of all three. This is consistent with current practice of experts, who prefer this cutting plane when conducting manual identifications based on anatomical features of wood samples.