Wood Strength Classification Based on RGB Color and Image Texture Using KNN Method

One of the most important factors for deciding the use of wood product is knowing its strength class. So far, the wood strength has been measured using relatively expensive laboratory tests both for purchase and rent. In addition, this test has also damaged the test wood. An alternative method that can be used to determine the strength class of woods is to observe their characteristics using the naked eye. However, this is also a tough challenge because it must be done by wood experts who truly memorize and understand the characteristics of each wood class. Therefore, an image processing method is needed to identify the strength of wood easily, cheaply and not to damage test wood. The object of this study consisted of seven types of wood: Acacia (Acacia mangium), Teak (Tectona grandis), Mahogany (Swietenia mahagoni), Silk Tree (Paraserianthes falcataria), Sengon Tekik, Rain Tree (Samanea Saman) and Coastal Cottonwood (Hibiscus tiliaceus). Based on the results of specific gravity measurement, the wood strength classes of the test wood were as follows : Acacia, Teak and Mahogany were Class II, Rain Tree and Coastal Cottonwood were Class III, while Silk Tree and Sengon Tekik were Class IV. The parameters used by the author to determine the strength of wood based on image processing are the RGB color and texture of the wood image. We use two feature extraction methods, the average RGB histogram value for each color channel and the static characteristics of the Gray-Level CoOccurrence Matrix (GLCM) method. The method used for feature selection is Recursive Feature Elimination Cross Validation (RFECV). While for data classification we use five algorithms: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Decision Tree (CART), Random Forest (RF), and Support Vector Machine (SVM). The results of the qualifications showed that the highest accuracy was obtained by the KNN of 95%. Therefore, this method can be used as an alternative to determine the strength of wood.