{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:25:06Z","timestamp":1776979506473,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2015,8,7]],"date-time":"2015-08-07T00:00:00Z","timestamp":1438905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"nsfc","doi-asserted-by":"publisher","award":["610011024, 61273243, 51407095"],"award-info":[{"award-number":["610011024, 61273243, 51407095"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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 \u201cWE + PCA + FSCABC-FNN\u201d and \u201cWE + PCA + BBO-FNN\u201d methods achieve the same accuracy of 89.5%, higher than state-of-the-art approaches: \u201c(CH + MP + US) + PCA + GA-FNN \u201d of 84.8%, \u201c(CH + MP + US) + PCA + PSO-FNN\u201d of 87.9%, \u201c(CH + MP + US) + PCA + ABC-FNN\u201d of 85.4%, \u201c(CH + MP + US) + PCA + kSVM\u201d of 88.2%, and \u201c(CH + MP + US) + PCA + FSCABC-FNN\u201d 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.<\/jats:p>","DOI":"10.3390\/e17085711","type":"journal-article","created":{"date-parts":[[2015,8,7]],"date-time":"2015-08-07T10:25:21Z","timestamp":1438943121000},"page":"5711-5728","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":118,"title":["Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization"],"prefix":"10.3390","volume":"17","author":[{"given":"Shuihua","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China"},{"name":"Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4870-1493","authenticated-orcid":false,"given":"Yudong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China"},{"name":"Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Genlin","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiquan","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianguo","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Electronic Information & Electrical Engineering, Shanghai Jiaotong University,  Shanghai 200030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.foodres.2014.03.012","article-title":"Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review","volume":"62","author":"Zhang","year":"2014","journal-title":"Food Res. 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