Improving the quality of sorting wood chips by scanning and machine vision technology

Abstract

Improving the quality of sorting wood waste is the main problem in the timber industry from the point of view of saving energy resources and preserving the environment, associated with the intensity of forest harvesting. Depending on the required quality characteristics, the sorting of wood chips makes it possible to determine their further use in production or utilization as a fuel. This paper presents the results of the development of a novel approach to sorting wood chips on a conveyor belt using machine learning and scanning technology. The proposed methodology includes functions to analyze the fractional size distribution among wood chips and rot detection. It shows that once a defective unit is detected, the quality control system will automatically remove it from the conveyor belt while it is moving. The minimization of wood waste will reduce logging intensity and increase the profitability of lumber enterprises.