Application of data mining to forest operations planning

Abstract

In Uruguay, mechanized forestry harvesting for industrial purposes is carried out using modern equipment. They are capable of record a wealth of information that can be exploited in the decision making process and improve operations. Some approaches from data mining field, as decision trees, are an alternative to analyze large volumes of data and determine incidence factors. In this work, it was proposed to analyze how different variables of the forest harvest (DBH, species, shift and operator) affect the productivity of a forest harvester. Data were collected automatically by a forest harvester working on plantations of Eucalyptus spp. in Uruguay. The results show that DBH is the most influential factor in productivity.