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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Mardhiah Awalludin
MAHSA University
Computer Science & IT

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Deep learning approaches for accurate wood species recognition Heshalini Rajagopal; Nicky Christian; Devika Sethu; Mohd. Azwan Ramlan; Hanis Farhah Jamahori; Mardhiah Awalludin; Norul Ashikin Norzain; Renuka Devi Rajagopal; Narayanan Ganesh
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26640

Abstract

Wood species identification is a crucial task in various industries, including forestry, woodworking, and conservation. Traditional methods rely on manual expertise, which can be time-consuming and error prone. Hence, an automatic wood species recognition system is developed in this study using deep learning (DL) models. In this study, three deep convolutional neural network (CNN) architectures, SqueezeNet, GoogLeNet, and ResNet-50 was tailored for wood species classification. The accuracy of the DL models was evaluated in recognizing fifty different wood species. Additionally, the wood species images were altered using JPEG Compression, Gaussian Blur, Salt and Pepper, and Speckle noises to assess the models' performance in identifying the wood species from the distorted images. Results show that the ResNET-50 based wood recognition system is the most accurate model to recognise the wood species. The implications of this research extend to forestry management, quality control in woodworking industries, and the preservation of endangered wood species in conservation efforts.
Co-Authors Devika Sethu Hanis Farhah Jamahori Heshalini Rajagopal Mohd. Azwan Ramlan Narayanan Ganesh Nicky Christian Norul Ashikin Norzain Renuka Devi Rajagopal