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

Authors

  • Anindita Safna Oktaria 1) Computer Vision Research Group, Research Center for Informatics, Indonesian Institute of Sciences (LIPI) 2) Department of Telecommunication Engineering, Telkom University
  • Esa Prakasa Computer Vision Research Group, Research Center for Informatics, Indonesian Institute of Sciences (LIPI)
  • Efri Suhartono Department of Telecommunication Engineering, Telkom University

DOI:

https://doi.org/10.25126/jitecs.201943155

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%.

Author Biography

Anindita Safna Oktaria, 1) Computer Vision Research Group, Research Center for Informatics, Indonesian Institute of Sciences (LIPI) 2) Department of Telecommunication Engineering, Telkom University

I graduated from Dept of Telecommunication Engineering, Telkom University in 2019. My research was conducted at Computer Vision Research Group, Research Center for Informatics LIPI. The research is supervised by LIPI researcher.

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Published

2019-12-20

How to Cite

Oktaria, A. S., Prakasa, E., & Suhartono, E. (2019). Wood Species Identification using Convolutional Neural Network (CNN) Architectures on Macroscopic Images. Journal of Information Technology and Computer Science, 4(3), 274–283. https://doi.org/10.25126/jitecs.201943155

Issue

Vol. 4 No. 3: Desember 2019

Section

Articles

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