Neural Networks for Unique Wood Identification


In 2013, the Federal Republic of Germany committed itself to preventing the import and distribution of illegal wood species and to implementing the Washington Convention on International Trade in Endangered Species of Wild Fauna and Flora in this regard. This requires, among other things, an unambiguous identification of the wood species in the various materials and products made from wood. Within the scope of the research project, an optical image recognition system for the determination of wood species in fibrous materials (pulp, paper/products) is to be developed in order to be able to fulfill the required declaration obligations with regard to species identification in accordance with the European Timber Trade Regulation (EUTR) in the trade and to be able to verify them on a larger scale. The clear recognition and delimitation of these structural characteristics for a doubtless wood species identification currently requires a well-founded scientific training/expertise and, above all, access to proven reference preparations. Our cooperation partner - the Thünen Institute for Wood Research - has more than 50,000 reference preparations at its disposal.
The task of the PhD will be to develop neural networks for unambiguous wood identification based on high-resolution microscope images and to compare these with microstructure analysis methods. The PhD will take place in close cooperation with the experts of the Thünen Institute.
The PhD project of Lars Nieradzik will directly link the expertise of the Thünen Institute with the development of neural networks in order to optimally select and parameterize complex algorithms, correctly evaluate intermediate results and correct errors as early as possible. To this end, an AI assistant is being developed that learns the expected workflow and data flow, as well as expected intermediate results and typical error patterns.