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.

A comprehensive review on wood chip moisture content assessment and prediction

“…The obtained results demonstrated that the prediction accuracy of the brightness-based model (R 2 > 0.9) is greater than that of the texture-based model (R 2 > 0.8). Apart from RGB (Red Green Blue) images [45,77,78], laser scanned image [50,79] and hyperspectral images [36] have also been explored for wood chip quality control. Gillespie et al [36] combined the NIR hyperspectral imaging with chemometrics to determine the moisture content and specific energy of wood chips and achieved a prediction accuracy of R 2 = 0.94 and RMSE of 1.11%.…”

“…Scanned image [46] Determining size distribution of poplar wood chip Partial least square regression (PLS-R) Image and multivariate data [47] Sorting wood wastes Multi-layer perceptron (MLP) Image [78] Predicting mixture ratio of two wood chip species with varying properties Linear and non-linear regression Image [45] Sorting wood wastes A graphical-analytical method involving segmentation of binary image obtained through Otsu's method Laser scan [79]…”