Wood Species Identification Based on Gray Level Co-Occurrence Matrix (GLCM) Features on Macroscopic Images

Muhammad Ghiffaari Ilham Ramadhan; Bambang Sugiarto; Okta Dwi Mulya; Defti Septian Chairulsyah; Adyanto Syahrizal; Gunawan Gunawan; Riffa Haviani Laluma; Rini Nuraini Sukmana; Teguh Wiharko

doi:10.17529/jre.v21i1.41078

Wood Species Identification Based on Gray Level Co-Occurrence Matrix (GLCM) Features on Macroscopic Images

Muhammad Ghiffaari Ilham Ramadhan, Bambang Sugiarto, Okta Dwi Mulya, Defti Septian Chairulsyah, Adyanto Syahrizal, Gunawan Gunawan, Riffa Haviani Laluma, Rini Nuraini Sukmana, Teguh Wiharko

Abstract

Wood is an incredibly valuable resource, particularly for everyday living. To fully harness the advantages of wood, it must focus on two key considerations. Firstly, it is imperative to consistently utilize wood sourced from sustainably managed forests. Secondly, we must explore techniques that maximize the utilization of every part of the tree. One technique for meeting these considerations is to create a wood identification system. This system can be used for quickly inspecting wood species. In wood identification, it is essential to consider specific characteristics and physical properties of wood. Manual identification will depend on the examination of wood anatomists’ eye and will require a significant amount of time. In accordance with these situations, a computer vision-based system can address this condition. Therefore, feature extraction is necessary to extract the features of wood characteristics from the wood image. This research aims to propose a method for wood species identification based on Gray Level Co-occurrence Matrix (GLCM) features to extract important information about wood characteristics from macroscopic wood images. For the classifier, the Random Forest algorithm is proposed for the identification of the machine learning model. Five wood species images will be used in this research, with each wood sample being presented as a macroscopic image. The total dataset used was 750 images, with each wood species having 150 images. The result showed that the Model C (90/10) training data ratio demonstrates good performance in classifying wood species from the macroscopic images. The model achieved a peak accuracy of 0.81 and correctly predicted all test images. This study indicates that the Random Forest model can be an effective classifier for wood species identification.

Keywords

feature extraction; GLCM; macroscopic wood image; random forest; wood identification

Full Text:

PDF

References

D. Maier, “Building materials made of wood waste a solution to achieve the sustainable development goals,” Materials, 14(24), 7638, 2021.

M.C. Wiemann, “Chapter 2: Characteristics and availability of commercially important woods.,” In: Wood handbook—wood as an engineering material. General Technical Report FPL-GTR-282. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 45 pp, 2021.

H. Ergun, “Wood identification based on macroscopic images using deep and transfer learning approaches”. PeerJ, 12, e17021, 2024.

B. Sugiarto et al., "Wood identification based on histogram of oriented gradient (HOG) feature and support vector machine (SVM) classifier," 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, pp. 337-341, 2017.

J. L. Silva, R. Bordalo, J. Pissarra, & P. de Palacios, “Computer vision-based wood identification: A review,” Forests, 13(12), 2041, 2022.

F. Ruffinatto, F. Negro, & A. Crivellaro, “The Macroscopic Structure of Wood,” Forests, 14(3), 644, 2023.

X. He, D. M. Pelt, J. Gao, B. Gravendeel, P. Zhu, S. Chen, J. Qiu, & F. Lens, “Machine learning-based wood anatomy identification: towards anatomical feature recognition,” IAWA Journal, 1(aop), 1-19, 2024.

Breiman, L, “Random forests”, Machine learning, 45, 5-32, 2001.

A. Parmar, R. Katariya, & V. Patel, “A review on random forest: An ensemble classifier,” International conference on intelligent data communication technologies and internet of things (ICICI) 2018 (pp. 758-763). Springer International Publishing, 2019.

S. W. Hwang, & J. Sugiyama, “Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review,” Plant Methods, 17(1), 47, 2021.

L. K. Seng and T. Guniawan, "An Experimental Study on the Use of Visual Texture for Wood Identification Using a Novel Convolutional Neural Network Layer," 2018 8th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, 2018.

G. Figueroa-Mata, E. Mata-Montero, J. C. Valverde-Otarola and D. Arias-Aguilar, "Using Deep Convolutional Networks for Species Identification of Xylotheque Samples," 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), San Carlos, Costa Rica, pp. 1-9, 2018.

Y. Sun, Q. Lin, X. He, Y. Zhao, F. Dai, J. Qiu, & Y. Cao, Y, “Wood species recognition with small data: A deep learning approach,” International Journal of Computational Intelligence Systems, 14(1), 1451-1460, 2021.

F. Wu, R. Gazo, E. Haviarova, & B. Benes, “Wood identification based on longitudinal section images by using deep learning,” Wood Science and Technology, 55, 553-563, 2021.

A. R. de Geus, S. F. D. Silva, A. B. Gontijo, F. O. Silva, M. A. Batista, & J. R. Souza, “An analysis of timber sections and deep learning for wood species classification,” Multimedia Tools and Applications, 79(45), 34513-34529, 2020.

M. L. Hadiwidjaja, P. H. Gunawan, E. Prakasa, Y. Rianto, B. Sugiarto, B., R. Wardoyo, R. Damayanti, K. Sugiyanto, L. M. Dewi, & V. F. Astutiputri, “Developing wood identification system by local binary pattern and hough transform method,” Journal of Physics: Conference Series (Vol. 1192, p. 012053). IOP Publishing, 2019.

D. V. Souza, J. X. Santos, H. C. Vieira, T. L. Naide, S. Nisgoski, & L. E. S. Oliveira, “An automatic recognition system of Brazilian flora species based on textural features of macroscopic images of wood,” Wood Science and Technology, 54(4), 1065-1090, 2020.

S. W. Hwang, K. Kobayashi, S. Zhai, & J. Sugiyama, “Automated identification of Lauraceae by scale-invariant feature transform,” Journal of Wood Science, 64(2), 69-77, 2018.

N. R. Rosli, U. Khairuddin, R. Yusof, H. A Ghapar, A. S. M. Khairuddin, & N. A, Ahmad, “Online system for automatic tropical wood recognition,” ELEKTRIKA-Journal of Electrical Engineering, 18(3-2), 1-6, 2019.

E. Arkin, N. Yadikar, X. Xu, A. Aysa, & K. Ubul, “A survey: object detection methods from CNN to transformer,” Multimedia Tools and Applications, 82:21353–21383, 2023.

A. A. Elngar, M. Arafa, A. Fathy, B. Moustafa, O. Mahmoud, M. Shaban, & N. Fawzy, “Image classification based on CNN: a survey,” Journal of Cybersecurity and Information Management, 6(1), 18-50, 2021.

D. Bhatt, C. Patel, H. Talsania, J.Patel, R. Vaghela, S. Pandya, K. Modi, & H. Ghayvat, “CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope,” Electronics, 10(20):2470, 2021.

A. Mathew, A. Antony, Y. Mahadeshwar, T. Khan, & A. Kulkarni, A, “Plant disease detection using GLCM feature extractor and voting classification approach,” Materials Today: Proceedings, 58, 407-415, 2022.

A. Fahrurozi, S. Madenda, & D. Kerami, “Wood texture features extraction by using glcm combined with various edge detection methods,” Journal of Physics: Conference Series (Vol. 725, No. 1, p. 012005). IOP Publishing, 2016.

H. Rajagopal, N. Mokhtar, A. S. M. Khairuddin, W. Khairunizam, Z. Ibrahim, A. B. Adam, & W. A. B. M. Mahiyidin, “Gray level co-occurrence matrix (GLCM) and gabor features based no-reference image quality assessment for wood images,“” Proc. Int. Conf. Artif. Life and Rob.(ICAROB) (pp. 736-741), 2021.

S. Neethu and L. Baby Syla, "Wood Species Recognition Using Machine Learning," 2021 Fourth International Conference on Microelectronics, Signals & Systems (ICMSS), Kollam, India, pp. 1-6, 2021.

A. R. Hakim, Y. Handayani, G. F. Shidiq and A. Z. Fanani, “Classification Types of Wood Furnitures Using Gray Level Co-Occurrence Matrix and K-Nearest Neighbor,” 2021 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarangin, Indonesia, pp. 300-306, 2021.

M. A. Yulianto, & A. Fadlil, “Wood Type Identification System using Naive Bayes Classification,” Control Systems and Optimization Letters, 1(3), 139-143, 2023.

X. Pei, Y. hong Zhao, L. Chen, Q. Guo, Z. Duan, Y. Pan, & H. Hou, “Robustness of machine learning to color, size change, normalization, and image enhancement on micrograph datasets with large sample differences”, Materials & Design, 232, 112086, 2023.

B. Yin, Machine Learning-Based Methods for the Segmentation of Scanning Electron Microscopy Images of Fine-Grained Shale Samples (Doctoral dissertation, The University of Texas at Arlington), 2022.

R. M. Dyke, & K. Hormann, “Histogram equalization using a selective filter,” The visual computer, 39(12), 6221-6235, 2023.

C. I. Ossai, & N. Wickramasinghe, “GLCM and statistical features extraction technique with Extra-Tree Classifier in Macular Oedema risk diagnosis,” Biomedical Signal Processing and Control, 73, 103471, 2022.

M. Yogeshwari, & G. Thailambal, “Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks,” Materials Today: Proceedings, 81, 530-536, 2023.

X. Zhang, J. Cui, W. Wang, & C. Lin, “A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm,” Sensors, 17(7), 1474, 2017.

V. P. Singh, A. Srivastava, D. Kulshreshtha, A. Chaudhary, & R. Srivastava, “Mammogram classification using selected GLCM features and random forest classifier,” International Journal of Computer Science and Information Security (IJCSIS), 14(6), 82-87, 2016.

J. Rout, S. K. Das, P. Mohalik, S. Mohanty, C. K. Mohanty, & S. K. Behera,”Glcm based feature extraction and medical x-ray image classification using machine learning techniques,” International Conference on Intelligent Systems and Machine Learning (pp. 52-63). Cham: Springer Nature Switzerland, 2022.

S. Dong,& Z. Huang,”A Brief Theoretical Overview of Random Forests[J],” Integrated technology, 2 (1): 1-7, 2013.

J. Liu, F. Lv, & P. Di, “Identification of sunflower leaf diseases based on random forest algorithm, “ International Conference on Intelligent Computing, Automation and Systems (ICICAS) (pp. 459-463). IEEE, 2019.

L. J. Chencho, H. Hao, R. Wang, & L. Li, “Development and application of random forest techniques for element level structural damage quantification,” Structural Control and Health Monitoring, 28(3), e2678, 2021.

K. Shah, H. Patel, D. Sanghvi, & M. Shah, “A comparative analysis of logistic regression, random forest and KNN models for the text classification,” Augmented Human Research, 5(1), 12, 2020.

S. B. Atitallah, M. Driss, & I. Almomani, “A novel detection and multi-classification approach for IoT-malware using random forest voting of fine-tuning convolutional neural networks,” Sensors, 22(11), 4302, 2022.

D. Krstinić, M. Braović, L. Šerić., & D. Božić-Štulić, “multi-label classifier performance evaluation with confusion matrix,” Computer Science & Information Technology, 1, 1-14, 2020.

DOI: https://doi.org/10.17529/jre.v21i1.41078

Article Metrics

Abstract view : 0 times
PDF - 0 times

Refbacks

There are currently no refbacks.

View My Stats

Jurnal Rekayasa Elektrika (JRE) is published under license of Creative Commons Attribution-ShareAlike 4.0 International License.