Tropical Timber Identification using Backpropagation Neural Network

International Conference on Computing and Applied Informatics 2016

物理学;计算机科学

Siregar, B.^1 ; Andayani, U.^1 ; Fatihah, N.^1 ; Hakim, L.^2 ; Fahmi, F.^3

Faculty of Computer Science and Information Technology, University of Sumatera Utara, Medan, Indonesia^1

Faculty of Forestry, University of Sumatera Utara, Medan, Indonesia^2

Dept. Electrical Engineering, University of Sumatera Utara, Medan, Indonesia^3

关键词: Back propagation neural networks;    Data extraction;    Digital image;    Gray level co-occurrence matrix;    Human errors;    Manual identification;    Pre-processing;    Tropical timber;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/801/1/012051/pdf
DOI  :  10.1088/1742-6596/801/1/012051

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Each and every type of wood has different characteristics. Identifying the type of wood properly is important, especially for industries that need to know the type of timber specifically. However, it requires expertise in identifying the type of wood and only limited experts available. In addition, the manual identification even by experts is rather inefficient because it requires a lot of time and possibility of human errors. To overcome these problems, a digital image based method to identify the type of timber automatically is needed. In this study, backpropagation neural network is used as artificial intelligence component. Several stages were developed: A microscope image acquisition, pre-processing, feature extraction using gray level co-occurrence matrix and normalization of data extraction using decimal scaling features. The results showed that the proposed method was able to identify the timber with an accuracy of 94%.