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ABSTRACT
Given the upward trend of deforestation in the world, improving the quality of wood waste sorting operations is a major challenge in forestry from the perspective of energy saving and environmental conservation. The quality of wood chips defines their further application, whether it is production or fuel. The second case study presents a new approach to the problem of sorting wood chips for increasing their quality using machine learning and laser scanning technology. The proposed methodology includes functions to analyse the fractional size distribution among wood chips and rot detection. It shows that once a defective chip 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.
Acknowledgements
The work was carried out within the confines of the scientific school ‘Advances in lumber industry and forestry’.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Data will be available on request.