# Detection of Fungal Infections on the Wood Surface Using LTM Imaging
## 1 Introduction
However, the industrial application of artificial intelligence algorithms for high-resolution images may prove difficult or impossible in many tasks due to the required time. Three-dimensional imaging methods are also used in wood quality assessment tasks [25]. Within these methods, one can distinguish ways which require the product to stop during image registration. Methods that enable the image registration of a product moving on the processing line can also be identified. Depending on the imaging method, vision systems will allow the registration of images with different resolutions [26]. The choice of method is also influenced by the time taken to build a three-dimensional image which, for industrial applications, is one of the critical parameters determining the choice of vision system type. Analysis of 3D images allows the assessment of surface defects and knots localization on the surface of boards regardless of the variation in the color of the observed surface [27]. It also allows the assessment of the geometric structure of the board and tree bark [28]. 3D imaging enables the measurement of a much larger number of parameters in terms of spatial and surface dimensions realized in terms of quality control of the materials used in production. Industry standards define the quality of wood used in manufacturing wood products. For the production of wood pallets according to EPAL and CHEP standards [29; 30], the presence of fungal infections is considered a defect in the material, defined as 'rot'. Our research and analysis of the literature in this area indicate that blue-green fungi are most commonly found on the surface of the wood [31]. Depending on the season, up to a dozen blue stain fungi were identified. For example, one of these is _Ceratocysts pinicola_, which is visible as characteristic, with extensive blue-black discoloration often reaching up to several centimeters deep into the wood. Examples of identified infected surfaces on material supplied from sawmills are summarized in Figure 1.  
The research described in this paper aimed to develop a system to identify fungal infections on a wood surface. In addition, it was assumed that the system would work under industrial conditions allowing the inspection of all boards entering the production line. The identification and rejection of infected boards are intended to improve the quality of the final product. It is also designed to eliminate the cost of repairing a finished product for which infected material has been used. Due to the production cycle, imaging of the plank surface should not be longer than 1 s, and the analysis and identification of infected planks should also be carried out in less than 0.5 s. In addition, it was assumed that it should be possible to determine the dimensions of the boards on the image during the inspection. Achieving such an objective required the development of an imaging method that enables the entire task of inspecting the surface quality of planks to be carried out in flow-through mode. This means that the object being inspected, a plank, is not stopped in the production line. The planks are moved on a conveyor belt, and an image of the surface is captured during their movement. The image is then analyzed, after which a surface evaluation result is issued. This result is used by the sorting system, allowing defective boards to be removed from the process. This research was carried out as part of a project to improve manufacturing quality by eliminating defective materials, such as fungus-infected boards.