Deep Learning for Plant Species Classification using Leaf Vein Morphometric

Tan, Jing Wei and Chang, Siow Wee and Kareem, Sameem Abdul and Yap, Hwa Jen and Yong, Kien Thai (2018) Deep Learning for Plant Species Classification using Leaf Vein Morphometric. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17 (1). pp. 82-90. ISSN 1545-5963

Full text not available from this repository.
Official URL: https://doi.org/10.1109/TCBB.2018.2848653

Abstract

An automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new CNN-based method named D-Leaf was proposed. The leaf images were pre-processed and the features were extracted by using three different Convolutional Neural Network (CNN) models namely pre-trained AlexNet, fine-tuned AlexNet, and D-Leaf. These features were then classified by using five machine learning techniques, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest-Neighbor (k-NN), Naïve-Bayes (NB), and CNN. A conventional morphometric method computed the morphological measurements based on the Sobel segmented veins was employed for benchmarking purposes. The D-Leaf model achieved a comparable testing accuracy of 94.88 percent as compared to AlexNet (93.26 percent) and fine-tuned AlexNet (95.54 percent) models. In addition, CNN models performed better than the traditional morphometric measurements (66.55 percent). The features extracted from the CNN are found to be fitted well with the ANN classifier. D-Leaf can be an effective automated system for plant species identification as shown by the experimental results. © 2004-2012 IEEE.

Item Type: Article
Uncontrolled Keywords: artificial neural network; classification; convolutional network; deep learning; feature extraction; leaf vein morphometric; Tropical tree
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QH Natural history
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Computer Science & Information Technology
Faculty of Engineering
Faculty of Science > Institute of Biological Sciences
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 16 Jun 2020 01:13
Last Modified: 16 Jun 2020 01:13
URI: http://eprints.um.edu.my/id/eprint/24834

Actions (login required)

View Item View Item