Classification of Peanut Images Based on Multi-features and SVM

被引：21
|
作者：
Li, Zhenbo [1 ,2 ,3 ]
Niu, Bingshan [1 ]
Peng, Fang [1 ]
Li, Guangyao [1 ]
Yang, Zhaolu [4 ]
Wu, Jing [1 ]
机构：
[1] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
[2] Minist Agr, Key Lab Agr Informat Acquisit Technol, Beijing 100083, Peoples R China
[3] Beijing Engn & Technol Res Ctr Internet Things Ag, Beijing 100083, Peoples R China
[4] Lu Dong Univ, Coll Informat & Elect Engn, Yantai, Peoples R China
来源：
IFAC PAPERSONLINE | 2018年 / 51卷 / 17期
基金：
对外科技合作项目（国际科技项目）;
关键词：
image classification; HOG; Aspect ratio; Hu invariant moment; SVM; RECOGNITION; QUALITY; COLOR;
D O I：
10.1016/j.ifacol.2018.08.110
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article provides a method for accurate classification of peanuts. Peanuts can be classified into three categories, including one peanut, two peanuts and three peanuts. Because different peanuts have different prices. The characteristics of peanut images were extracted by three different methods including the convolution neural network of aspect ratio, HOG and Hu invariant moment, and then classifying peanut images respectively by the SVM (support vector machine). The accuracy rate of the aspect ratio + SVM algorithm, HOG+SVM algorithm, Hu invariant moment +SVM algorithm respectively is 96.72%, 81.97% and 81.97%, realize the industrialization of peanut classification. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：726 / 731
页数：6
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