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IJMLC 2019 Vol.9(3): 363-367 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.3.811

Vietnamese Herbal Plant Recognition Using Deep Convolutional Features

Anh H. Vo, Hoa T. Dang, Bao T. Nguyen, and Van-Huy Pham

Abstract—Herbal plant image identification is able to help users without specialized knowledge about botany and plan systematics to find out the information of herbal plans, thus it has become an interdisciplinary focus in both botanical taxonomy and computer vision. A computer vision aided herbal plan identification system has been developed to meet the demand of recognizing and identifying herbal plants rapidly. In this paper, the first herbal plant image dataset collected by mobile phone in natural scenes is presented, which contains 10,000 images of 10 herbal plant species in Vietnam. A VGG16-based deep learning model consisting of 5 residual building blocks is used to extract features from the images. A comparative evaluation of seven classification methods using the same deep convolutional feature extraction method is presented. Experiments on our collected dataset demonstrate that deep learning features worked well with LightGBM classification method for herbal plant recognition in the natural environment with a recognition rate of 93.6%.

Index Terms—Deep feature, deep learning, herbal plant, plant identification.

Anh H. Vo and Hoa T. Dang are with the Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam (e-mail: vohoanganh@ tdtu.edu.vn, dathaha86@gmail.com,).
Bao T. Nguyen is with the Faculty of Information Technology, University of Education and Technology, Ho Chi Minh City, Vietnam (e-mail: baont@hcmute.edu.vn).
Huy V. Pham is with the AI Lab, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam (e-mail: phamvanhuy@ tdtu.edu.vn).

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Cite: Anh H. Vo, Hoa T. Dang, Bao T. Nguyen, and Van-Huy Pham, "Vietnamese Herbal Plant Recognition Using Deep Convolutional Features," International Journal of Machine Learning and Computing vol. 9, no. 3, pp. 363-367, 2019.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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