Fruit category classification via an eight-layer convolutional neural network with parametric rectified linear unit and dropout technique

Shui Hua Wang*, Yi Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)

Abstract

In this paper, we apply an improved deep convolutional neural network (CNN) in fruit category classification, which is a hotspot in computer vision field. We created an 8-layer deep convolutional neural network, and utilized parametric rectified linear unit to take the place of plain rectified linear unit, and placed dropout layer before each fully-connected layer. Data augmentation was used to help avoid overfitting. Our 8-layer deep convolutional neural network secured an overall accuracy of 95.67%. This proposed 8-layer method performs better than five state-of-the-art methods using traditional machine learning methods and one state-of-the-art CNN method.

Original languageEnglish
Pages (from-to)15117-15133
Number of pages17
JournalMultimedia Tools and Applications
Volume79
Issue number21-22
DOIs
Publication statusPublished - 1 Jun 2020
Externally publishedYes

Keywords

  • Computer vision
  • Convolutional neural network
  • Data augmentation
  • Deep learning
  • Dropout
  • Fruit category classification
  • Parametric rectified linear unit

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