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
*
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
(This article belongs to the Special Issue Machine Learning and Entropy: Discover Unknown Unknowns in Complex Data Sets)
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.
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Keywords:
Shannon entropy; machine learning; fruit classification; wavelet transform; feed-forward neural network; artificial bee colony; biogeography-based optimization
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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.
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