Tree bark prediction along the bole through the support vector regression technique

Abstract

Tree bark plays a protective role by surrounding the wood of a tree like a cloak. Due to its chemical composition and the possibility of its use in various fields, such as pharmaceuticals, landscape architecture, etc., tree bark receives much attention having outstanding importance for industrial utilization and markets. Tree bark is considered a valuable forest product, along with the wood volume. Thus, the accurate prediction of the bark quantity that a tree can produce is of utmost importance for the sustainable management of the forests. For this reason, the knowledge of its quantities, further enables the accurate prediction of the plain wood volume that can be produced by the forest, as well. Because of the nonlinear nature of this biological variable, its accurate quantification is a very complicated problem. Artificial intelligent methods have shown the potential to reliably predict biological variables that are non-linear in nature. In this work, the support vector regression methodology, as a nonlinear nonparametric machine learning approach, is tested for the accurate prediction of the tree bark factor in every different height of the tree bole, through easily obtained measurements on trees.