Rahillda Nadhirah Norizzaty Rahiddin, Ummi Raba'ah Hashim, Kasturi Kanchymalay, Lizawati Salahuddin, Martina Ali, Aji Prasetya Wibawa
This study explores timber defect classification using features extracted from Colour Uniform Local Binary Patterns (CULBP) for four timber species: Rubberwood, KSK, Merbau, and Meranti, evaluating eight common defects such as bark pocket, blue stain, borer holes, brown stain, knot, rot, split, and wane. A series of colour feature sets were extracted from individual and combined RGB channels, with classification performed via an Artificial Neural Network (ANN). Results demonstrate that RGB channels without extensive colour normalisation deliver optimal accuracy, achieving 90.2% - higher than all other channels. Compared to previous studies using grayscale images and other LBP variations, our approach outperformed prior models like Daubechies Wavelet and LBP (85%) and basic LBP methods (65.4%), confirming that integrating RGB colour channels significantly enhances classification. Among timber species, Rubberwood achieved the highest accuracy, with particularly strong results for Rubberwood Rot (92.2%) and Merbau Hole (95.6%), underscoring the advantages of colour-based feature extraction in timber defect classifcation. © (2025), (International Association of Engineers). All rights reserved.
Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia; Department of Software Engineering, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia; Department of Software Engineering, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia; Software Engineering Department, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia; Information Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia; Universitas Negeri Malang (UM), Indonesia
