Although conifers have very homogeneous wood anatomical structure, some of their features allow differentiation of genera or groups of genera. Softwood anatomy is reviewed and the authors have added contributions, grouping features in five blocks (axial tracheids, axial parenchyma, rays, resin canals and mineral inclusions), with special attention to their diagnostic value at the family, genus or species levels. The discussion addresses, in axial tracheids: growth rings, intercellular spaces, pit arrangement, presence of scalloped tori, torus extensions, notches, warty layer, organic deposits, helical and callitroid thickenings, bars of Sanio and trabeculae; in axial parenchyma: presence or absence, arrangement and type of transverse end walls; in rays: composition, type of horizontal and end walls and cross-field pitting; in resin canals: epithelium and the presence of normal or traumatic resin canals; and in mineral inclusions: presence or absence. Wood structure is characterised at the family level, discussing the main elements that can be used for diagnosis at the genus level or in groups of genera.

The conversion of processes controlled by human expertise and know-how into automated operations is the goal of a wide range of modern research. This study reports on a blind test in which twelve unknown samples were analyzed by human experts and an AI system developed to check the genera of hardwoods used in paper production. Trained to detect hardwood cells in microscopic images of maceration slides, it assigns them to the nine most commonly used hardwood species. Softwoods were also added to the samples, to make the test as realistic as possible. Human experts achieved an accuracy of 0.96 for recognizing a genus contained in a sample. The two-stage machine recognition system achieved an accuracy of 0.79 at the defined threshold values of 0.7 for object recognition and 0.75 for genus probability. Human experts failed to recognize some genera contained in the samples, whereas the AI models did not miss any genus at these thresholds. A comparison of the results revealed the strengths and weaknesses of the independent methods. This study is an important step towards optimizing the automated image recognition system developed to support the implementation of the European Union Deforestation Regulation (EUDR) with regard to deforestation-free products/supply chains.