De Blaere, R.,  Lievens, K.,  Van den Bulcke, J.,  Verwaeren, J.,  De Mil, T. & Beeckman, H. 2022. ‘Wood identification techniques for combatting illegal logging: Applications on macroscopic wood anatomical assessment using digital classification keys and AI’. Joint 6th Annual Meeting on Plant Ecology and Evolution & COBECORE meeting. Book of abstracts. Meise : AMPEE6&COBECORE.

Résumé de colloque

Wood identification is a key step in the enforcement of laws and regulations aiming at combatting illegal timber trade. It is a major concern especially for countries with species-rich forest resources. The most used, cheapest and most generally applicable method for wood identification is the anatomical assessment by trained experts. Such assessment includes the observation of features on tissues and cells on the transversal, tangential and radial plane and aims at scoring diagnostic features to characterize the botanical taxon. Traditionally, this process requires a laboratory setting to prepare microscopic thin sections, and large collections of reference material to identify a wood specimen unto species level. Some features do not require the use of laboratory equipment to observe them as they are visible with the unaided eye or a handheld macro-lens. Those ‘macroscopic’ features can be used to indicate the genus or species of the specimen, and thereby provide a cheap way to identify wood. Nowadays, modern technology can provide simplified ways in order to aid macroscopic wood identification such as digital identification keys. These are essentially decision trees, using large databases of textual descriptions on anatomical features or other distinguishing characteristics. They are consulted by giving the observed features as input and result in a list of possible genera or species. The main advantages of classification keys are their speed and flexibility, although they still require training of the user in recognition of macroscopic features. AI is another example of modern technology that can simplify identification, as learning to use a picture snapping app or device is easy in comparison to the long and difficult training on wood anatomy. Machine learning and specifically deep learning can be used to identify the botanical taxon of specimens by taking images of the wood surface and using Convolutional Neural networks to classify them