Wood Type Identification System using Naive Bayes Classification

Muhammad Anas Yulianto, Abdul Fadlil

Abstract


Wood, a forest product and natural resource, is a raw material used to make household goods. Some types of wood have almost the same pattern or structure. Wood quality varies greatly depending on the tree species and the environmental conditions in which it grows. This makes it challenging to identify the type of wood, especially for wooden furniture users. Therefore, wood classification is essential to ensure that the wood used meets the required quality standards and requirements. Automatic classification of wood using image processing has several advantages and can make it easier for humans. One of the image processing methods for wood classification is the Naïve Bayes method. Feature extraction technique using GLCM using contrast, correlation, energy, and homogeneity attributes. The GLCM methods can be combined to create a system design to distinguish five wood species using an image-based wood type identification system. The study results have successfully designed a system to determine five types of wood using the framework of an image-based wood type identification system. An application system has been produced to distinguish five types of wood using the framework of an image-based wood type identification system with the GLCM feature extraction method and the Naive Bayes classification method. The application system successfully identified wood species with a test accuracy rate of 88%.

Keywords


Naive Bayes; Wood Classification; GLCM; MATLAB

Full Text:

PDF

References


A. Alam, “Possibilities and Apprehensions in the Landscape of Artificial Intelligence in Education,” 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), pp. 1-8, 2021, https://doi.org/10.1109/ICCICA52458.2021.9697272.

A. Pirc Barčić, M. Kitek Kuzman, T. Vergot, and P. Grošelj, “Monitoring consumer purchasing behavior for wood furniture before and during the COVID-19 pandemic,” Forests, vol. 12, no. 7, p. 873, 2021, https://doi.org/10.3390/f12070873.

H. Kolya and C. W. Kang, “Hygrothermal treated paulownia hardwood reveals enhanced sound absorption coefficient: An effective and facile approach,” Applied Acoustics, vol. 174, p. 107758, 2021, https://doi.org/10.1016/j.apacoust.2020.107758.

R. Yadav and T. Mahara, “An exploratory study to investigate value chain of Saharanpur wooden carving handicraft cluster,” International Journal of System Assurance Engineering and Management, vol. 9, pp. 147-154, 2018, https://doi.org/10.1007/s13198-016-0492-5.

M. Sedliačiková, M. Moresová, P. Aláč, and D. Malá, “What is the supply and demand for coloured wood products? An empirical study in Slovakian practice,” Forests, vol. 12, no. 5, p. 530, 2021, https://doi.org/10.3390/f12050530.

E. Sesana, A. S. Gagnon, A. Bonazza, and J. J. Hughes, “An integrated approach for assessing the vulnerability of World Heritage Sites to climate change impacts,” Journal of cultural heritage, vol. 41, pp. 211-224, 2020, https://doi.org/10.1016/j.culher.2019.06.013.

I. J. Ansotegui, et al., “IgE allergy diagnostics and other relevant tests in allergy, a World Allergy Organization position paper,” World allergy organization journal, vol. 13, no. 2, p. 100080, 2020, https://doi.org/10.1016/j.waojou.2019.100080.

E. K. S. Nambiar, C. E. Harwood, and D. S. Mendham, “Paths to sustainable wood supply to the pulp and paper industry in Indonesia after diseases have forced a change of species from acacia to eucalypts,” Australian Forestry, vol. 81, no. no. 3, pp. 148-161, 2018, https://doi.org/10.1080/00049158.2018.1482798.

H. N. Ho, “Material recognition based on thermal cues: Mechanisms and applications,” Temperature, vol. 5, no. 1, pp. 36-55, 2018, https://doi.org/10.1080/23328940.2017.1372042.

E. Umberfield, A. A. Ghaferi, S. L. Krein, and M. Manojlovich, “Using incident reports to assess communication failures and patient outcomes,” The Joint Commission Journal on Quality and Patient Safety, vol. 45, no. 6, pp. 406-413, 2019, https://doi.org/10.1016/j.jcjq.2019.02.006.

S. Tan, S. Zhong and P. Chirarattananon, “A One-Step Visual–Inertial Ego-Motion Estimation Using Photometric Feedback,” in IEEE/ASME Transactions on Mechatronics, vol. 27, no. 1, pp. 12-23, 2022, https://doi.org/10.1109/TMECH.2021.3057887.

J. Juola, A. Hovi, and M. Rautiainen, “Classification of tree species based on hyperspectral reflectance images of stem bark,” European Journal of Remote Sensing, pp. 1-15, 2020, https://doi.org/10.1080/22797254.2022.2161420.

A. Almusaed, A. Almssad, A. Alasadi, I. Yitmen, and S. Al-Samaraee, “Assessing the Role and Efficiency of Thermal Insulation by the “BIO-GREEN PANEL” in Enhancing Sustainability in a Built Environment,” Sustainability, vol. 15, no. 13, p. 10418, 2023, https://doi.org/10.3390/su151310418.

S. Mourtzinis, et al., “Soybean response to nitrogen application across the United States: A synthesis-analysis,” Field Crops Research, vol. 215, pp. 74-82, 2018, https://doi.org/10.1016/j.fcr.2017.09.035.

J. Li, R. Ma, Z. Cao, K. Xue, J. Xiong, M. Hu, and X. Feng, “Satellite detection of surface water extent: A review of methodology,” Water, vol. 14, no. 7, p. 1148, 2022, https://doi.org/10.3390/w14071148.

A. V. Kachavimath, S. V. Nazare and S. S. Akki, “Distributed Denial of Service Attack Detection using Naïve Bayes and K-Nearest Neighbor for Network Forensics,” 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 711-717, 2020, https://doi.org/10.1109/ICIMIA48430.2020.9074929.

S. Sharma, M. Bhatt and P. Sharma, “Face Recognition System Using Machine Learning Algorithm,” 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 1162-1168, 2020, https://doi.org/10.1109/ICCES48766.2020.9137850.

T. I. Trishna, S. U. Emon, R. R. Ema, G. I. H. Sajal, S. Kundu and T. Islam, “Detection of Hepatitis (A, B, C and E) Viruses Based on Random Forest, K-nearest and Naïve Bayes Classifier,” 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-7, 2019, https://doi.org/10.1109/ICCCNT45670.2019.8944455.

M. A. Arasi, E. -S. M. El-Horbaty and E. -S. A. E. -D. El-Dahshan, “Classification of Dermoscopy Images Using Naïve Bayesian and Decision Tree Techniques,” 2018 1st Annual International Conference on Information and Sciences (AiCIS), pp. 7-12, 2018, https://doi.org/10.1109/AiCIS.2018.00015.

S. K. Maliha, R. R. Ema, S. K. Ghosh, H. Ahmed, M. R. J. Mollick and T. Islam, “Cancer Disease Prediction Using Naive Bayes,K-Nearest Neighbor and J48 algorithm,” 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-7, 2019, https://doi.org/10.1109/ICCCNT45670.2019.8944686.

H. Hasanli and S. Rustamov, “Sentiment Analysis of Azerbaijani twits Using Logistic Regression, Naive Bayes and SVM,” 2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT), pp. 1-7, 2019, https://doi.org/10.1109/AICT47866.2019.8981793.

K. Agarwal and T. Kumar, “Email Spam Detection Using Integrated Approach of Naïve Bayes and Particle Swarm Optimization,” 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 685-690, 2018, https://doi.org/10.1109/ICCONS.2018.8662957.

A. M. Rahat, A. Kahir and A. K. M. Masum, “Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset,” 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), pp. 266-270, 2019, https://doi.org/10.1109/SMART46866.2019.9117512.

K. Chawgien and S. Kiattisin, “Machine learning techniques for classifying the sweetness of watermelon using acoustic signal and image processing,” Computers and Electronics in Agriculture, vol. 181, p. 105938, 2021, https://doi.org/10.1016/j.compag.2020.105938.

S. Ghazal, W. S. Qureshi, S. Khan, J. Iqbal, N. Rashid, and M. I. Tiwana, “Analysis of visual features and classifiers for Fruit classification problem,” Computers and Electronics in Agriculture, vol. 187, p. 106267, 2021, https://doi.org/10.1016/j.compag.2021.106267.

T. Badriyah, N. Sakinah, I. Syarif and D. R. Syarif, “Machine Learning Algorithm for Stroke Disease Classification,” 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey, 2020, pp. 1-5, 2020, https://doi.org/10.1109/ICECCE49384.2020.9179307.

C. Zhang, P. Patras and H. Haddadi, “Deep Learning in Mobile and Wireless Networking: A Survey,” in IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2224-2287, 2019, https://doi.org/10.1109/COMST.2019.2904897.

K. Shankar, Y. Zhang, Y. Liu, L. Wu and C. -H. Chen, “Hyperparameter Tuning Deep Learning for Diabetic Retinopathy Fundus Image Classification,” in IEEE Access, vol. 8, pp. 118164-118173, 2020, https://doi.org/10.1109/ACCESS.2020.3005152.

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), pp. 300-306, 2021, https://doi.org/10.1109/iSemantic52711.2021.9573247.

C. Brischke and G. Alfredsen, “Wood-water relationships and their role for wood susceptibility to fungal decay,” Applied microbiology and biotechnology, vol. 104, pp. 3781-3795, 2020, https://doi.org/10.1007/s00253-020-10479-1.

Y. Huang, Y. Ji, and W. Yu, “Development of bamboo scrimber: A literature review,” Journal of Wood Science, vol. 65, no. 1, pp. 1-10, 2019, https://doi.org/10.1186/s10086-019-1806-4.

X. Sun, M. He, and Z. Li, “Novel engineered wood and bamboo composites for structural applications: State-of-art of manufacturing technology and mechanical performance evaluation,” Construction and Building Materials, vol. 249, p. 118751, 2020, https://doi.org/10.1016/j.conbuildmat.2020.118751.

C. Irawan, E. N. Ardyastiti, D. R. I. M. Setiadi, E. H. Rachmawanto and C. A. Sari, “A Survey: Effect of the Number of GLCM Features on Classification Accuracy of Lasem Batik Images using K-Nearest Neighbor,” 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 33-38, 2018, https://doi.org/10.1109/ISRITI.2018.8864443.

D. Indra, H. M. Fadlillah, Kasman and L. B. Ilmawan, “Rice Texture Analysis Using GLCM Features,” 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), pp. 1-5, 2021, https://doi.org/10.1109/ICECET52533.2021.9698594.

V. Durgamahanthi, J. Anita Christaline, and A. Shirly Edward, “GLCM and GLRLM based texture analysis: application to brain cancer diagnosis using histopathology images,” In Intelligent Computing and Applications: Proceedings of ICICA 2019, pp. 691-706, 2019, https://doi.org/10.1007/978-981-15-5566-4_61.

M. H. Daneshvari, E. Nourmohammadi, M. Ameri, and B. Mojaradi, “Efficient LBP-GLCM texture analysis for asphalt pavement raveling detection using eXtreme Gradient Boost,” Construction and Building Materials, vol. 401, p. 132731, 2023, https://doi.org/10.1016/j.conbuildmat.2023.132731.

Y. Yuan, S. Li, X. Zhang and J. Sun, “A Comparative Analysis of SVM, Naive Bayes and GBDT for Data Faults Detection in WSNs,” 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp. 394-399, 2018, https://doi.org/10.1109/QRS-C.2018.00075.

V. R. Balaji, S. T. Suganthi, R. Rajadevi, V. K. Kumar, B. S. Balaji, and S. Pandiyan, “Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier,” Measurement, vol. 163, p. 107922, 2020, https://doi.org/10.1016/j.measurement.2020.107922.

P. Phoenix, R. Sudaryono, and D. Suhartono, “Classifying promotion images using optical character recognition and Naïve Bayes classifier,” Procedia Computer Science, vol. 179, pp. 498-506, 2021, https://doi.org/10.1016/j.procs.2021.01.033.

A. Akbar et al., “Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications,” in IEEE Access, vol. 6, pp. 10015-10027, 2018, https://doi.org/10.1109/ACCESS.2018.2804623.




DOI: https://doi.org/10.59247/csol.v1i3.52

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Muhammad Anas Yulianto, Abdul Fadlil

 
Control Systems and Optimization Letters
ISSN: 2985-6116
Website: https://ejournal.csol.or.id/index.php/csol
Email: alfian_maarif@ieee.org
Publisher: Peneliti Teknologi Teknik Indonesia
Address: Jl. Empu Sedah No. 12, Pringwulung, Condongcatur, Kec. Depok, Kabupaten Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia