Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic abc and biogeography-based optimization

Shuihua Wang, Yudong Zhang*, Genlin Ji, Jiquan Yang, Jianguo Wu, Ling Wei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

132 Citations (Scopus)

Abstract

Fruit classification is quite difficult because of the various categories and similar shapes and features of fruit. In this work, we proposed two novel machine-learning based classification methods. The developed system consists of wavelet entropy (WE), principal component analysis (PCA), feedforward neural network (FNN) trained by fitness-scaled chaotic artificial bee colony (FSCABC) and biogeography-based optimization (BBO), respectively. The K-fold stratified cross validation (SCV) was utilized for statistical analysis. The classification performance for 1653 fruit images from 18 categories showed that the proposed "WE + PCA + FSCABC-FNN" and "WE + PCA + BBO-FNN" methods achieve the same accuracy of 89.5%, higher than state-of-the-art approaches: "(CH + MP + US) + PCA + GA-FNN " of 84.8%, "(CH + MP + US) + PCA + PSO-FNN" of 87.9%, "(CH + MP + US) + PCA + ABC-FNN" of 85.4%, "(CH + MP + US) + PCA + kSVM" of 88.2%, and "(CH + MP + US) + PCA + FSCABC-FNN" of 89.1%. Besides, our methods used only 12 features, less than the number of features used by other methods. Therefore, the proposed methods are effective for fruit classification.

Original languageEnglish
Pages (from-to)5711-5728
Number of pages18
JournalEntropy
Volume17
Issue number8
DOIs
Publication statusPublished - 2015
Externally publishedYes

Keywords

  • Artificial bee colony
  • Biogeography-based optimization
  • Feed-forward neural network
  • Fruit classification
  • Machine learning
  • Shannon entropy
  • Wavelet transform

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