In the paper, it is proposed to transfer a food industry`s machine learning knowledge to wood or construction industry. The universal machine vision system has great potential for the scalability. In modern industrial processes, fast and efficient detection of defects plays a crucial role in quality control.In most industrial processes, the defect detection process still relies on the visual inspection of trained workers with low detection efficiency and precision. Wood or metal defect detection increases the automation of the industry, making it less labour intensive, less costly and with improved efficiency. During the project, we proved that quality control inspection system with Machine Learning technology developed for the industry can be scaled up and using the same technology stack moved to the wood and construction industry. The main difference is the size, form and speed of conveyors. During the project, the inspection system achieved the necessary functionality and precision. A further scalability opportunity of the system using Machine Learning is obvious, requiring less time and labour than conventional quality control methods.