# Detection of Fungal Infections on the Wood Surface Using LTM Imaging
## 1 Introduction  
The industry, which uses wood in various forms, relies on material supplied by sawmills. One of the critical tasks associated with quality control of the provided material is the control of defects present in the material. Defects come in many different forms. One can distinguish between defects related to the machining, which include all kinds of lack of dimensions required, i.e., faults in boards' width, length and thickness and defects on milled or drilled surfaces. Natural defects can also be identified, such as the presence of knots and internal cracks that disqualify the material. The types of defects and their classification depend on the industry using wood as a material. This paper presents the results of a study and a system developed for the wood pallet production industry. Material defects are defined by the quality standards applicable to European Pallet Association (EPAL) and Commonwealth Handling Equipment Pool (CHEP). The rapid increase in production efficiency of process lines used in the woodworking industry is also linked to the automation of the quality control process for materials and manufactured products [1]. Statistical process control (SPC) is insufficient due to increasing customer demands. In the age of automation and industry digitalization, quality control of all products leaving the production line is required. The dynamic development of cyber-physical systems makes it possible to realize continuous production control and observation in production management systems of critical parameters describing the quality of materials and products [2]. Currently, manufacturers of specialized machines are forced to equip the machines with control systems that carry out selected inspection tasks directlyat the production stations. For example, studies are carried out to optimize the cutting of boards in sawmills, to control the surface of the wood in the furniture industry and monitoring of the absence of structural defects in boards as a construction material [3; 4; 5; 6; 7]. In addition, quality control of the finished products just before shipment to the customer is also realized to improve the companies' financial performance [8]. Methods for quality control of wood materials have been developed for many years to evaluate the internal parameters of wood and assess the dimensions and surfaces of wood. Based on thermal radiation, imaging is used to determine wood damage by fungi and insects [9; 10]. Methods based on ultrasound detection allow the detection of internal defects in the wood [11]. Similarly, internal defects can be detected using computed tomography [12; 13]. Wood defects visible on the surface are imaged using monochrome or colour cameras that allow 2D image acquisition. This type of imaging is used to look for knots, cracks or other surface defects [14]. The development of surface defect classification methods was based on edge analysis, area segmentation techniques or parameters describing areas in the image [15]. However, the high variability occurring on the wood surface in terms of changes in surface colour, changes in defect shape and colour, and variability in the geometric structure of the wood surface made the construction of a universal defect inspection system significantly difficult [16; 17]. With the development of information technology, imaging and various artificial intelligence algorithms have been applied to wood surface analysis tasks [18; 19]. These methods have completely changed the approach to identifying wood defects. The possibility of learning with different types of artificial intelligence algorithms from images of real defects occurring on wood has given great flexibility in their identification on surfaces [20]. The combination of classical methods of wood image analysis and methods using artificial intelligence algorithms allows for the realization of a wide range of parameters describing wood and product surface parameters used in the Smart Industry [13; 21; 22; 23; 24].