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

Entropy

Shuihua Wang¹  Yudong Zhang¹  Genlin Ji¹  Jiquan Yang⁴  Jianguo Wu²  Ling Wei³ 

[1] School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China; E-Mails:;College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; E-Mail:;School of Electronic Information & Electrical Engineering, Shanghai Jiaotong University, Shanghai 200030, China; E-Mail:;Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China; E-Mail:

关键词: Shannon entropy;    machine learning;    fruit classification;    wavelet transform;    feed-forward neural network;    artificial bee colony;    biogeography-based optimization;   

DOI  :  10.3390/e17085711

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.