[1]陈姗姗,宁纪锋,彭艺伟,等.基于近红外高光谱图像的苹果轻微损伤检测[J].智能系统学报,2013,8(04):356-360.[doi:10.3969/j.issn.1673-4785.201304041]
 CHEN Shanshan,NING Jifeng,PENG Yiwei,et al.Detection of slight bruises on apples using near-infrared hyperspectral image[J].CAAI Transactions on Intelligent Systems,2013,8(04):356-360.[doi:10.3969/j.issn.1673-4785.201304041]
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基于近红外高光谱图像的苹果轻微损伤检测(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第8卷
期数:
2013年04期
页码:
356-360
栏目:
学术论文—智能系统
出版日期:
2013-08-25

文章信息/Info

Title:
Detection of slight bruises on apples using near-infrared hyperspectral image
文章编号:
1673-4785(2013)04-356-05
作者:
陈姗姗宁纪锋 彭艺伟张叶
西北农林科技大学 信息工程学院,陕西 杨凌 712100
Author(s):
CHEN Shanshan NING Jifeng PENG Yiwei ZHANG Ye
College of Information Engineering, Northwest A&F University, Yangling 712100, China
关键词:
高光谱图像轻微损伤苹果缺陷检测波段比不均匀二次差分
Keywords:
hyperspectral image slight bruises apple defect detection band ratio asymmetric second difference
分类号:
TP391.41
DOI:
10.3969/j.issn.1673-4785.201304041
文献标志码:
A
摘要:
针对苹果轻微损伤时,基于可见光的机器视觉方法难以有效检测的缺点,开展了近红外高光谱图像的苹果轻微损伤检测研究.首先,用900~1 700 nm近红外波段范围对轻微损伤苹果高光谱成像,图像显示损伤部分与正常部分区别明显.其次,采用特征波段比方法和不均匀二次差分方法对损伤苹果光谱图像进行处理,增强损伤处与正常位置的可分性.最后,利用3种分割方案,对损伤部分进行自动分割.对50个含轻微损伤和正常的苹果进行分割,实验结果表明,不均匀二次差分方法的损伤检测准确率为92%,比主成分分析法和波段比方法具有更高的检测准确率,为轻微损伤苹果的准确检测提供了一种新的方法.
Abstract:
A research of apple slight bruises was conducted by using hyperspectral images, aimed at solving the difficulty of the traditional defect detection method of machine vision. This study is in part based on the fact that visible light faces great challenges on it. First, the hyperspectral images of slight bruise apples between 900 and 1700 nm are acquired by a hyperspectral imaging system. It can be found that the differences between the normal part and the bruise part are obvious. Next, we analyzed the hyperspectral images via the feature band ratio method and asymmetric second difference method to improve the divisibility of the normal part and the bruise part. Finally, the bruise parts were automatically segmented from the normal part with three defect detection methods. The experimental results show that the accuracy of detecting slight bruises on the 50 apples using asymmetric second difference method is 92%, which is higher than the principal component analysis and band ratio methods. Therefore, the work provides a new method to detect the slight bruise apples accurately.

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备注/Memo

备注/Memo:
收稿日期:2013-04-15.    网络出版日期:2013-08-28. 
基金项目:国家自然科学基金资助项目(61003151);中央高校基本科研业务费专项基金资助项目(QN2013055,QN2013062);国家级大学生创新创业训练计划资助项目(1210712132).
通信作者:宁纪锋. E-mail: jf_ning@sina.com.
 作者简介:
陈姗姗,女,1990年生,硕士研究生,主要研究方向为机器视觉在农业信息化域中的应用.
宁纪锋,男,1975年生,副教授,硕士生导师.主要研究方向为计算机视觉与模式识别.发表学术论文30余篇,其中被SCI检索7篇、EI检索15篇.
更新日期/Last Update: 2013-09-27