Illegal logging is one of the consequences of rising global demand for lumber. The European Union Timber Regulation (EUTR), the predecessor of the EUDR, entered into force back in 2013 with the aim of curbing the unlawful use of wood. Since then, commercial enterprises have been required to document the types of wood used in their products and their origins, thereby demonstrating the legal origins of the wood used in goods they import into the EU market. The same applies to wood products such as particle board, fiberboard, paper, and cardboard. But how can the types of wood used in fiber materials be identified without a doubt?
As things currently stand, responsibility for examining wood products falls to people such as the employees of the Thünen Institute, a research institute in the portfolio of the Federal Ministry of Food and Agriculture (BMEL). They receive numerous product samples from industry and government agencies so they can check the types of wood used – and the numbers are rising. The samples are then sent for expert analysis under a microscope, which is an extremely time-consuming process. With paper and fiberboard, the wood cells are separated from the material, dyed and then prepared on a slide. The cells can then be classified based on their appearance when viewed through a microscope. But because this preparation and examination process is so time-consuming and more and more samples are coming in for testing, the specialists can only handle a limited number of expert reports. A new AI-based analytical tool is being developed to help with this situation by relieving some of the workload on highly qualified experts, accelerating and automating the examination process, and enabling fast, efficient controls. Researchers from Fraunhofer ITWM and the Thünen Institute of Wood Research in Hamburg have teamed up in the KI_Wood-ID project, using machine learning to develop the new automated image recognition system to identify types of wood. The project is funded by the German Federal Ministry of Food and Agriculture (BMEL).