Machine Learning-based Algorithm for Type of Wood Identification

The remarkable developments in machine learning have made it feasible to use wood pictures to identify wood, a new area of research in wood science that is steadily working to the development for wood identification automatically to satisfy the demands of the market and the wood sector. There are many different tree species and the wood made from each of them has distinct structural, mechanical and physical characteristics. Due to the variations in the structure and characteristics of wood, a wide range of objects with various applications and aesthetics can be made from wood. It is necessary to employ the right species in the right places in order to use this priceless substance effectively. To help minimize misclassification of wood species in the wood industries, a novel application in the field of computer vision called intelligent wood species recognition is being researched. With the use of photographs taken of wood samples or features noticed, woods recognition implements the process of identifying the various species of wood. In this study, the LBP (Local binary pattern) histogram is utilized to identify the various wood species by extracting characteristics from improved wood photos. The objective of the wood recognition system is to determine whether it is feasible to develop a system that can automatically recognize wood based on its anatomy. Due to the technique's rotation invariance and tolerance for gray scale fluctuations the result achieved thus far shows a high recognition rate and accuracy indicating that it is very promising for practical applications.