Discriminant method approach for harvesting forest operations

Abstract

Forest harvesting operations are complex resolution problems where different factors of different nature intervene. These operations are affected by the nature of the trees to be harvested, the environment where they are planted, the operator who performs the operation and the shift in which it is performed, among other aspects. These factors affect the productivity of the harvest, which, in turn, being the first link in the forestry supply chain, affects the rest of the links. Poor management of harvest operations can lead to critical setbacks and delays in the forestry supply chain. In this work, it is proposed to develop productivity prediction models that allow adequately estimating productivity considering the simultaneous impact of all the factors or variables that intervene. For this, the data collected automatically by the harvesters are analyzed using the linear discriminant method. The results allow us to infer that the approach is adequate to generate these models, particularly when the target set to be predicted is partitioned.