基于NIR高光谱成像技术的长枣虫眼无损检测

吴龙国; 何建国; 刘贵珊; 贺晓光; 王伟; 王松磊; 李丹

doi:10.3788/fgxb20133411.1527

您当前的位置：

首页 >

文章列表页 >

基于NIR高光谱成像技术的长枣虫眼无损检测

发光学应用及交叉前沿 | 更新时间：2020-08-12

- 基于NIR高光谱成像技术的长枣虫眼无损检测
- Non-destructive Detection of Insect Hole in Jujube Based on Near-infrared Hyperspectral Imaging
- 发光学报 2013年34卷第11期页码：1527-1532
- 作者机构：
  
  1. 宁夏大学农学院, 宁夏银川 750021
  2. 宁夏大学物理电气信息学院,宁夏银川,750021
- 作者简介：
- 基金信息：
  
  国家自然科学基金(31060233)();国家科技支撑计划(2012BAF07B06)();2011年度宁夏回族自治区科技攻关计划资助项目()
- DOI：10.3788/fgxb20133411.1527
  中图分类号： TN27
- 收稿日期：2013-07-07，
  
  修回日期：2013-09-06，
  
  纸质出版日期：2013-11-10
- 稿件说明：
移动端阅览
吴龙国, 何建国, 刘贵珊, 贺晓光, 王伟, 王松磊, 李丹. 基于NIR高光谱成像技术的长枣虫眼无损检测[J]. 发光学报, 2013,34(11): 1527-1532

WU Long-guo, HE Jian-guo, LIU Gui-shan, HE Xiao-guang, WANG Wei, WANG Song-lei, LI Dan. Non-destructive Detection of Insect Hole in Jujube Based on Near-infrared Hyperspectral Imaging[J]. Chinese Journal of Luminescence, 2013,34(11): 1527-1532
吴龙国, 何建国, 刘贵珊, 贺晓光, 王伟, 王松磊, 李丹. 基于NIR高光谱成像技术的长枣虫眼无损检测[J]. 发光学报, 2013,34(11): 1527-1532 DOI： 10.3788/fgxb20133411.1527.

WU Long-guo, HE Jian-guo, LIU Gui-shan, HE Xiao-guang, WANG Wei, WANG Song-lei, LI Dan. Non-destructive Detection of Insect Hole in Jujube Based on Near-infrared Hyperspectral Imaging[J]. Chinese Journal of Luminescence, 2013,34(11): 1527-1532 DOI： 10.3788/fgxb20133411.1527.

摘要

为了研究快速识别虫眼枣与正常枣的有效方法

利用特征波长主成分分析法结合波段比算法进行虫眼枣识别。首先

利用NIR高光谱成像系统采集130个长枣(50个正常、80个虫眼枣)图像

提取并分析不同类型长枣特征区域的平均光谱曲线

对970~1 670 nm范围内的光谱数据进行主成分分析

确定7个特征波长(990

1 028

1 109

1 160

1 231

1 285

1 464 nm)。然后

对长枣图像做主成分分析

选择PC2图像进行虫眼识别

虫眼与正常枣的识别率分别为67.5%、100%。为了进一步提高虫眼枣的识别率

采用波段比(R1231/R1109)对未识别的虫眼枣进行再次识别

识别率提高到90%。结果表明

基于NIR高光谱成像技术的检测方法对虫眼枣识别是可行的

同时也为多光谱成像技术应用于在线检测长枣品质提供了理论依据。

Abstract

In order to study an effective method for quickly detecting the intact jujubes and insect hole jujubes

principal component analysis (PCA) on the optimal wavelengths combined with band ratio were applied to identify the insect hole jujubes. First

the hyperspectral images of jujube in the spectral region between 900 nm and 1 700 nm were acquired for 130 jujube samples (50 intact

80 insect hole)

and obtained region of interests (ROIs) as an average spectral of various jujubes

the wavelengths between 970 nm and 1 670 nm were analyzed and combined with PCA method to determine seven feature wavelengths (

i.e.

990

1 028

1 109

1 160

1 231

1 285

1 464 nm). Next

the PCA method was performed again based on important wavelengths and the second principal component (PC2) was used to classify insect hole jujubes. The classification rate of insect hole jujubes and intact jujubes was 67.5%

100%

respectively. To improve identification rate

band ratio (R1231/R1109) was utilized to distinguish the previously unidentified jujubes and the classification rate of insect hole jujubes was from 67.5% to 90%. The results show that the hyperspectral imaging technology can be used to effectively identify the insect hole jujubes

in the meantime

which can provide research basis for online detection of jujube quality using multispectral imaging technology.

关键词

Keywords

references

Hege E, O'Connell D, Johnson W, et al. Hyperspectral imaging for astronomy and space surveillance [J]. Proc. SPIE, 2003, 51:380-391.[2] Monteiro S, Minekawa Y, Kosugi Y, et al. Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery [J]. ISPRS J. Photogrammetry and Remote Sensing, 2007, 62(1):2-12.[3] Lyon R C, Lester D S, Lewis E N, et al. Near-infrared spectral imaging for quality assurance of pharmaceutical products:Analysis of tablets to assess powder blend homogeneity [J]. AAPS Pharm. Sci. Tech., 2002, 3(3):1-15.[4] Ferris D, Lawhead R, Dickman E, et al. Multimodal hyperspectral imaging for the noninvasive diagnosis of cervical neoplasia [J]. J. Lower Gen. Tract D, 2001, 5:65-72.[5] Xue L, Li J, Liu M H. Researches of hyperspectral imaging in the detection of surface bruising of pear [J]. Cere. Oils Proc.(粮油加工), 2009, 4:136-138 (in Chinese).[6] Li J B, Rao X Q, Ying Y B, et al. Detection of navel oranges canker based on hyperspectral imaging technology [J]. Transac. CSAE (农业工程学报), 2010, 26(8):222-228 (in Chinese).[7] Zhao J, Peng Y K, Zhao S W, et al. Detection of defects in apples based on hyperspectral imaging technology[J]. J. Food S. Q. (食品安全质量检测学报), 2012, 6(3):681-684 (in Chinese).[8] L Q, Tang M J. Detection of hidden bruise on Kiwi fruit using hyperspectral imaging and parallelepiped classification [J]. Procedia Environ. Sci., 2012, 12:1172-1179.[9] Wang J, Nakano K, Ohashi S, et al. Detection of external insect infestations in jujube fruit using hyperspectral reflectance imaging [J]. Biosys. Eng., 2011, 108:345-351.[10] Li J B, Rao X Q, Ying Y B. Advance on application of hyperspectral imaging to non-destructive detection of agricultural products external quality [J]. Spectrosc. Spect. Anal.(光谱学与光谱分析), 2011, 31(8):2021-2026 (in Chinese).[11] Polder G, Gerie W A M, Van de Heijden. Calibration and characterization of imaging spectrographs [J]. Near-Infrared Spectroscopy, 2003, 11:193-210.[12] Cai J R, Wang J H, Chen Q S, et al. Detection of rust in citrus by hyperspectral imaging technology and band ratio algorithm [J]. Transac. CSAE (农业工程学报), 2009, 25(1):127-131 (in Chinese).

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于高光谱图像信息融合的红提糖度无损检测

可见/近红外高光谱成像技术对长枣中葡萄糖含量的无损检测

高光谱成像结合SIS与RFS的蓝莓腐烂病检测

基于高光谱成像技术的土壤水分机理研究及模型建立

基于高光谱成像提取苹果糖度与硬度最佳波长