This research thesis explore the implementation of machine vision and artificial intelligence in automating the process of rating the quality of recycled wood. The goal is to contribute to the circular economy by promoting the reuse of wood in the construction industry. The thesis will focus on detecting visual defects such as nails, knots, and cracks on the surface of wood planks using a machine vision system integrated into a mobile station. It also includes a cost analysis of the required hardware and software. The research will utilize software tools such as Visual Studio code with Python 3.10.9 and Google Colab for training the AI model. Key hardware resources include a conveyor, wood samples with different qualities and defects, Raspberry Pi 4, Oak D lite camera, SD card, and a 5V power supply.The method described in this research study uses a machine vision system with a customized object identification neural network to find defects on the wood planks and measure them. Five distinct planks were used to test the object detection model, and the results were reported in a table. The model precision was discovered to be 72.6%. The neural network was shown to have a 67.0% accuracy rate. The model recall, which gauges how accurately it detected defects, was found to be 89.8%. The difficulty of identifying various defect types due to variances in appearance and imbalance in the training data is discussed in the study. The measurement of plank dimensions using machine vision methods is also evaluated in the paper. Five planks had their length, width, and depth measured, and the absolute and relative errors were computed. The width measurements had an acceptable average absolute error of 0.84 mm and a relative error of 0.92%. There was space for improvement in the depth measurements, which had an average absolute error of 1.8 mm and a relative error of 7.98%. The length measurements, on the other hand, revealed a higher absolute and relative inaccuracy, pointing to measurement algorithm limits. It is demonstrating the possibilities of employing AI for measuring and detecting defects in wood planks. It recognizes the length measurement as an area that needs further work and emphasizes the need for a more consistent dataset to increase the accuracy of the detectionproblem. The constraint of not taking into account the internal defects of the planks is also highlighted in the report, which also underlines the requirement for standards in recycled wood quality inspection for the advancement of circular economy activities.