Predicting Moisture Content on Wood Using Machine Learning Classification Methods.

: The growing demand for wood in several industry segments and for its economical value increased illegal deforestation in several countries. As a direct consequence, climate changes across the planet have been aggravated, which further increases the prominence and concern about the issue of deforestation. So that these potentially catastrophic effects can be mitigated, it is necessary to better use wood in production processes. In this sense, a key point is the variation of the moisture content of the wood as a function of storage time, since, as the wood logs are stored outdoors, they gradually begin to lose water. Dry wood usually cracks, which makes most of its use unfeasible – depending on the purpose – which can even lead to the disposal of the log. Considering that there is a direct relationship between moisture content and wood weight, this work aims to develop different possible solutions for this problem using explainable machine learning methods, contributing to the effectiveness in controlling the variation in moisture content and, consequently, to a better use in the production processes in which wood is used as a raw material.