Herb Classification with Convolutional Neural Network

Jia Wei Tan, Kian Ming Lim, Chin Poo Lee

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Herbs are plants with savory or aromatic properties that are widely used for flavoring, food, medicine or perfume. The worldwide use of herbal products for healthcare has increased tremendously over the past decades. The plethora of herb species makes recognizing the herbs remains a challenge. This has spurred great interests among the researchers on pursuing artificial intelligent methods for herb classification. This paper presents a convolutional neural network (CNN) for herb classification. The proposed CNN consists of two convolution layers, two max pooling layers, a fully-connected layer and a softmax layer. The ReLU activation function and dropout regularization are leveraged to improve the performance of the proposed CNN. A dataset with 4067 herb images was collected for the evaluation purposes. The proposed CNN model achieves an accuracy of above 93% despite the fact that some herbs are visually similar.

Original languageEnglish
Title of host publication3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665428996
DOIs
Publication statusPublished - 13 Sept 2021
Externally publishedYes
Event3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021 - Kota Kinabalu, Sabah, Malaysia
Duration: 13 Sept 202115 Sept 2021

Publication series

Name3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021

Conference

Conference3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021
Country/TerritoryMalaysia
CityKota Kinabalu, Sabah
Period13/09/2115/09/21

Keywords

  • CNN
  • convolutional neural network
  • Herb classification

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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