Abstract
This paper explains about identifying wood types using a macroscopic image on wood surfaces which have specfic characteristics, such as cross-section, radial, and tangential. Generally, on the identification process of wood types, traders and carpenters only do the checking which focuses on the cross-section part, it happened because of the difficulty of identifying the radial and the tangential wood surfaces. By using the convolutional neural network method, it can extract images with several layers, so that it is possible to do an identification process on all three wood surfaces. There are approximately 3,000 images which consist of 3 species of wood with each cross-section, radial and tangential surfaces. Identification results showed great potential even though there was a small amount of misclassification caused by similarities in different species and differences in similar species. Within the process, classification results obtained by the amount training accuracy 89\(\%\) and testing accuracy 96\(\%\) for the cross-section, 79\(\%\) for the radial and 88\(\%\) for the tangential planes. Thus, the identification of wood surfaces with high accuracy result was at the cross-section surface. abstract environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Barmpoutis, P., Dimitropoulos, K., Barboutis, I., Grammalidis, N., Lefakis, P.: Wood species recognition through multidimensional texture analysis. Comput. Electron. Agricul. 144, 241–248 (2018)
Deng, L., Yu, D., et al.: Deep learning: methods and applications. Found. Trends® Sign. Process. 7(3–4), 197–387 (2014)
Gonzalez, R.C.: Richard e. woods. Digit. Image Process. 2, 550–570 (2002)
Hadiwidjaja, M.L., Gunawan, P.H., Prakasa, E., Rianto, Y., Sugiarto, B., Wardoyo, R., Damayanti, R., Sugiyanto, K., Dewi, L.M., Astutiputri, V.F.: Developing wood identification system by local binary pattern and hough transform method. In: Journal of Physics: Conference Series. vol. 1192, p. 012053. IOP Publishing (2019)
Hafemann, L.G., Oliveira, L.S., Cavalin, P.: Forest species recognition using deep convolutional neural networks. In: 2014 22nd International Conference on Pattern Recognition, pp. 1103–1107. IEEE (2014)
Lainez, M.P.E.A., Bustamante, S.G.H., Orellana, G.C.: Deep learning applied to identification of commercial timber species from peru. In: 2018 IEEE XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pp. 1–4. IEEE (2018)
Liang, G., Hong, H., Xie, W., Zheng, L.: Combining convolutional neural network with recursive neural network for blood cell image classification. IEEE Access 6, 36188–36197 (2018)
Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of machine learning. MIT press (2018)
Salma, Gunawan, P.H., Prakasa, E., Damayanti, R., Sugiyama, J., et al.: Classification of Japanese fagaceae wood based on microscopic image analysis. In: 2019 7th International Conference on Information and Communication Technology (ICoICT), pp. 1–6. IEEE (2019)
Salma, Gunawan, P.H., Prakasa, E., Sugiarto, B., Wardoyo, R., Rianto, Y., Damayanti, R., Dewi, L.M., et al.: Wood identification on microscopic image with daubechies wavelet method and local binary pattern. In: 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 23–27. IEEE (2018)
Sugiarto, B., Prakasa, E., Wardoyo, R., Damayanti, R., Dewi, L.M., Pardede, H.F., Rianto, Y., et al.: Wood identification based on histogram of oriented gradient (HOG) feature and support vector machine (svm) classifier. In: 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 337–341. IEEE (2017)
Suyanto: Machine Learning Tingkat Dasar dan Lanjutan. Informatika (2018)
Yusof, R., Ahmad, A., Khairuddin, A.S.M., Khairuddin, U., Azmi, N.M.A.N., Rosli, N.R.: Transfer learning approach in automatic tropical wood recognition system. In: International Conference on Computational & Experimental Engineering and Sciences, pp. 1225–1233. Springer (2019)
Zufar, M., Setiyono, B., et al.: Convolutional neural networks untuk pengenalan wajah secara real-time. Jurnal Sains dan Seni ITS 5(2), 128862 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rostina, Gunawan, P.H., Prakasa, E. (2020). Identifying Wood Types Using Convolutional Neural Network. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives in Intelligent Systems. CoMeSySo 2020. Advances in Intelligent Systems and Computing, vol 1294. Springer, Cham. https://doi.org/10.1007/978-3-030-63322-6_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-63322-6_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63321-9
Online ISBN: 978-3-030-63322-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)