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Open AccessArticle

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

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School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China
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Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China
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College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China
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School of Electronic Information & Electrical Engineering, Shanghai Jiaotong University, Shanghai 200030, China
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Author to whom correspondence should be addressed.
Academic Editor: Andreas Holzinger
Entropy 2015, 17(8), 5711-5728; https://doi.org/10.3390/e17085711
Received: 22 May 2015 / Revised: 22 July 2015 / Accepted: 28 July 2015 / Published: 7 August 2015
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
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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.

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