Monday, September 2, 2024
The new European Deforestation Regulation (EUDR) aims to ensure that goods marketed in the EU do not contribute to deforestation. For instance, when a wood product enters the EU market, it must be accompanied by documentation detailing the wood species used in its production, along with proof of their legal origin. Depending on the material, even the initial verification of the declared wood type can be challenging. For example, paper requires a time-consuming examination by specialists. To streamline and expedite this process, a new AI-based analytical tool for identifying wood types is being developed.
Researchers from the Fraunhofer Institute for Industrial Mathematics ITWM are collaborating closely with the Thünen Institute of Wood Research to create this automated image recognition system, designed for large-scale verification of wood type declarations.
Illegal logging is a direct consequence of the increasing global demand for lumber. To combat this, the European Union Timber Regulation (EUTR), which preceded the EUDR, was implemented in 2013 with the goal of reducing the illegal use of wood. Since its enactment, businesses have been required to document the species of wood used in their products and verify their origins, ensuring that the wood imported into the EU market is legally sourced. This requirement extends to wood-based products such as particle board, fiberboard, paper, and cardboard. However, the challenge remains: how can the types of wood used in fiber materials be identified with absolute certainty?
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.
Algorithms implementation to decrease the illegal lumber trade worldwide
The researchers’ first area of focus in the project is hardwoods, especially those originating from plantations grown worldwide for cellulose production. Artificial intelligence can be used to determine the type of wood based on vascular tissue, which varies in cell structure, shape, and size. Using reference preparations from the Thünen Institute’s vast collection of wood samples, the researchers from Fraunhofer ITWM are training neural networks until the AI is capable of independently identifying and classifying the characteristic features of particular species so it can detect the types of wood present in the microscopic image of an unknown sample. Training for each different category, such as birch, beech, and poplar, takes place separately. In the individual images, the analytical tool first marks the cells that are key to identifying the particular types of wood.
“A sample is considered anomalous if it contains characteristics that don’t match the declared types of wood,” says Dr. Henrike Stephani, the KI_Wood-ID project manager and deputy head of the Image Processing department at Fraunhofer ITWM in Kaiserslautern.
The need to prevent deforestation around the world
An initial prototype of the analysis system has been trained on reference samples and is now capable of accurately identifying eleven types of hardwood. The next phase will focus on extending this capability to softwood identification. The prototype, equipped with a graphical user interface (GUI), is currently available for use by the Thünen Institute. In the long run, the AI-driven image recognition tool is intended to be deployed globally, assisting testing laboratories and government agencies in monitoring and regulating the international wood trade.
“Ultimately, deforestation and the illegal timber trade can only be prevented at the global level, so we hope testing organizations that are approved worldwide will be able to benefit from our system in the future,” Stephani concluded.
Source: Fraunhofer
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