Application of data mining to forest operations planning

ROSSIT, DANIEL ALEJANDRO; OLIVERA, ALEJANDRO; VIANA CÉSPEDES, VÍCTOR; BROZ, DIEGO

Conferencia; First International Conference on Agro Big Data and Decision Support Systems in Agriculture; 2017

In Uruguay, mechanized forestry harvesting for industrial purposes is carried out using modern equipment. They arecapable 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 anddetermine incidence factors. In this work, it was proposed to analyze how different variables of the forest harvest (DBH1, species, shift and operator) affect the productivity of a forest harvester. Data were collected automatically by a forestharvester working on plantations of Eucalyptus spp. in Uruguay. The results show that DBH is the most influential factorin productivity.