Next Article in Journal
Deformed Algebras and Generalizations of Independence on Deformed Exponential Families
Next Article in Special Issue
Using Multidimensional ADTPE and SVM for Optical Modulation Real-Time Recognition
Previous Article in Journal
New Region Planning in France? Better Order or More Disorder?
Article Menu

Export Article

Open AccessArticle
Entropy 2015, 17(8), 5711-5728; doi:10.3390/e17085711

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

1
School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China
2
Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China
3
College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
4
School of Electronic Information & Electrical Engineering, Shanghai Jiaotong University, Shanghai 200030, China
*
Author to whom correspondence should be addressed.
Academic Editor: Andreas Holzinger
Received: 22 May 2015 / Revised: 22 July 2015 / Accepted: 28 July 2015 / Published: 7 August 2015
View Full-Text   |   Download PDF [1374 KB, uploaded 7 August 2015]   |  

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. View Full-Text
Keywords: Shannon entropy; machine learning; fruit classification; wavelet transform; feed-forward neural network; artificial bee colony; biogeography-based optimization Shannon entropy; machine learning; fruit classification; wavelet transform; feed-forward neural network; artificial bee colony; biogeography-based optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wang, S.; Zhang, Y.; Ji, G.; Yang, J.; Wu, J.; Wei, L. Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization. Entropy 2015, 17, 5711-5728.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top