Wood Species Identification using Convolutional Neural Network (CNN) Architectures on Macroscopic Images

Author

Anindita Safna Oktaria, Esa Prakasa, Efri Suhartono

Abstract

Indonesia is a country that is very rich in tree species that grow in forests. Wood growth in Indonesia consists of around 4000 species that have different names and characteristics. These differences can determine the quality and exact use of each type of wood. The procedure of standard identification is currently still carried out through visual observation by the wood anatomist. The wood identification process is very in need of the availability of wood anatomists, with a limited amount of wood anatomist will affect the result and the length of time to make an identification. This thesis uses an identification system that can classify wood based on species names with a macroscopic image of wood and the implementation of the Convolutional Neural Network (CNN) method as a classification algorithm. Supporting architecture used is AlexNet, ResNet, and GoogLeNet. Architecture is then compared to a simple CNN architecture that is made namely Kayu30Net. Kayu30Net architecture has a precision performance value reaching 84.6%, recall 83.9%, F1 score 83.1% and an accuracy of 71.6%. In the wood species classification system using CNN, it is obtained that AlexNet as the best architecture that refers to a precision value of 98.4%, recall 98.4%, F1 score 98.3% and an accuracy of 96.7%.

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References


I. Kartasujana and Suherdie, “4000 Jenis Pohon di Indonesia dan Index 4000 Jenis Kayu Indonesia (Berdasar Nama Daerah).” Badan Penelitian dan Pengembangan Kehutanan, 1993.

T. Pulungan, “Terbesar di Dunia, Koleksi Kayu Perkuat Pangkalan Data Cadangan Karbon,” 2018. [Online]. Available: https://nasional.sindonews.com/read/1360730/15/terbesar-di-dunia-koleksi-kayu-perkuat-pangkalan-data-cadangan-karbon-1544111835.

E. Prakasa, H. F. Pardede, Y. Rianto, R. Damayanti, Krisdianto, and L. M. Dewi, “Development of Computer Vision Methods for Wood Identification,” no. September, 2017.

A. . G. R. Gunawan, S. R. I. Nurdiati, and Y. Arkeman, “Identifikasi Jenis Kayu Menggunakan Support Vector Machine Berbasis Data Citra Wood Type Identification Using Support Vector Machine Based on Image Data,” J. Ilmu Komput. Agri Inform., vol. 3, pp. 1–8, 2014.

I. Goodfellow, Y. Bengio, and A. Courville, “Deep Learning,” An MIT Press B., 2016.

I. Wendianto Notonogoro, “Pengenalan Plat Nomor Indonesia menggunakan Convolutional Neural Network,” 2018.

Mathworks, “Introducing Deep Learning with MATLAB,” Introd. Deep Learn. with MATLAB, p. 15.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” pp. 1097--1105, 2012.

C. Szegedy et al., “Going deeper with convolutions,” arXiv1409.4842 [cs], pp. 1–9, 2014.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” CoRR, vol. abs/1512.0, pp. 1–17, 2015.

H.-C. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285–98, 2016.

A. Veit, M. Wilber, and S. Belongie, “Residual Networks Behave Like Ensembles of Relatively Shallow Networks,” pp. 1–9, 2016.

Y. Wu et al., “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation,” pp. 1–23, 2016.

O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015.




DOI: http://dx.doi.org/10.25126/jitecs.201943155