Prediction of inner wood defects from outer bark shape

Abstract

The analysis of the internal structure of trees is highly important for both forest experts, biological scientists, and the wood industry. Traditionally, CT-scanners are considered as the most efficient way to get an accurate inner representation of the tree. However, this method requires an important investment and reduces the cost-effectiveness of this operation. Our goal is to design neural-network-based methods to predict the internal density of the tree from its external bark shape. We will compares different image-to-image (2D), volume-to-volume (3D) and Convolutional Long Short Term Memory based neural network architectures in the context of the prediction of the defect distribution inside trees from their external bark shape. Those models are trained on a synthetic dataset of 1800 CT-scanned look-like volumetric structures of the internal density of the trees and their corresponding external surface. Those different methods and approaches might potentially help in predicting the internal defect distribution of a real CT-scanned log from its external shape. However, identifying and extracting the most relevant and predictable internal defects is necessary before applying any deep-learning based method in order to predict the internal structure of the tree.