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基于高光谱技术与机器学习的新疆红枣品种鉴别

Identification of Xinjiang Jujube Varieties Based on Hyperspectral Technique and Machine Learning

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摘要

为实现对红枣品种的判别,利用高光谱技术并结合机器学习算法对金丝大枣、骏枣和滩枣这三个品种的新疆红枣进行研究。首先,分别利用多元散射校正(MSC)、标准正态变量变换(SNV)、一阶导(1-Der)和Savitzky-Golay(SG)平滑等数据预处理方法对原始光谱进行预处理,研究了预处理方法对建模的影响;然后,利用光谱-理化值共生距离法(SPXY)将样本集划分为校正集和预测集,基于线性判别分析(LDA)、K-最近邻分类(KNN)和支持向量机(SVM)算法对预处理后的全波段光谱建立红枣品种鉴别模型,结果显示,在多种预处理方法中,1-Der的处理效果最好;然后,结合主成分分析(PCA)、连续投影算法(SPA)和竞争性自适应重加权采样(CARS)等特征提取方法对全波段光谱进行特征波段的提取,再基于特征波段建立红枣品种鉴别模型,结果发现,在几种特征提取方法中,基于CARS所提特征波段建立的模型可以获得最高的鉴别准确率;最后,以SVM模型为例对模型运行时间进行了比较,结果发现,基于特征波段所建模型的运行时间远短于基于全波段所建模型的运行时间。

Abstract

To identify different Xinjiang jujube varieties, a hyperspectral technique and machine learning algorithms were employed to obtain and analyze the spectral data of Jinsi-jujube, Jun-jujube, and Tan-jujube. First, the original spectra were preprocessed using various data preprocessing methods, including multiplicative scatter correction (MSC), standard normal variate transformation (SNV), first-derivative (1-Der), and Savitzky-Golay (SG) smoothing. The effects of the preprocessing methods on modeling were investigated. Then, the samples were divided into calibration and prediction sets using sample set partitioning methods based on joint X-Y distance (SPXY). The jujube variety identification models were established based on linear discriminant analysis (LDA), K-nearest neighbor (KNN), and support vector machine (SVM) algorithms using the preprocessed full-band spectra. The results demonstrate that 1-Der outperformed other preprocessing methods mentioned above. Next, the characteristic bands were extracted from the full-band spectra using principal component analysis (PCA), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS). Then, the jujube variety identification models were established based on the characteristic bands. The CARS-based models achieved the highest accuracy in the models established based on several characteristic band extraction methods. Finally, taking the SVM model as an example, the model runtime was compared. The time required by the SVM model based on the characteristic bands was much shorter than the time required by the model based on the full-band spectra.

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中图分类号:O433.4

DOI:10.3788/CJL202047.1111002

所属栏目:光谱学

基金项目:国家自然科学基金、高等学校学科创新引智计划、深圳大学光电子器件与系统教育部/广东省重点实验室开放基金、瞬态光学与光子技术国家重点实验室开放基金;

收稿日期:2020-04-16

修改稿日期:2020-06-23

网络出版日期:2020-11-01

作者单位    点击查看

刘立新:西安电子科技大学物理与光电工程学院, 陕西 西安 710071中国科学院西安光学精密机械研究所瞬态光学与光子技术国家重点实验室, 陕西 西安 710119
何迪:西安电子科技大学物理与光电工程学院, 陕西 西安 710071
李梦珠:西安电子科技大学物理与光电工程学院, 陕西 西安 710071
刘星:深圳技术大学中德智能制造学院, 广东 深圳 518118
屈军乐:深圳大学物理与光电工程学院,光电子器件与系统教育部/广东省重点实验室, 广东 深圳 518060

联系人作者:刘立新(lxliu@xidian.edu.cn)

备注:国家自然科学基金、高等学校学科创新引智计划、深圳大学光电子器件与系统教育部/广东省重点实验室开放基金、瞬态光学与光子技术国家重点实验室开放基金;

【1】Goetz A F H, Vane G, Solomon J E, et al. Imaging spectrometry for earth remote sensing [J]. Science. 1985, 228(4704): 1147-1153.

【2】Tagesson T, Fensholt R, Guiro I, et al. Ecosystem properties of semiarid savanna grassland in West Africa and its relationship with environmental variability [J]. Global Change Biology. 2015, 21(1): 250-264.

【3】Amodio M L, Capotorto I. Chaudhry M M A, et al. The use of hyperspectral imaging to predict the distribution of internal constituents and to classify edible fennel heads based on the harvest time [J]. Computers and Electronics in Agriculture. 2017, 134: 1-10.

【4】Li C, Fan P, Jiang K, et al. Melon seed variety identification based on hyperspectral technology combined with discriminant analysis [J]. Bangladesh Journal of Botany. 2017, 46(3): 1153-1160.

【5】Ravikanth L, Jayas D S. White N D G, et al. Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products [J]. Food and Bioprocess Technology. 2017, 10(1): 1-33.

【6】Goto A, Nishikawa J, Kiyotoki S, et al. Use of hyperspectral imaging technology to develop a diagnostic support system for gastric cancer [J]. Journal of Biomedical Optics. 2015, 20(1): 016017.

【7】Markgraf W. Janssen M W W, Lilienthal J, et al. Hyperspectral imaging for ex-vivo organ characterization during normothermic machine perfusion [J]. European Urology Supplements. 2018, 17(2): e767.

【8】Liu L X, Li M Z, Zhao Z G, et al. Recent advances of hyperspectral imaging application in biomedicine [J]. Chinese Journal of Lasers. 2018, 45(2): 0207017.
刘立新, 李梦珠, 赵志刚, 等. 高光谱成像技术在生物医学中的应用进展 [J]. 中国激光. 2018, 45(2): 0207017.

【9】Chander S, Gujrati A, Abdul Hakeem K, et al. Water quality assessment of river ganga and chilika lagoon using AVIRIS-NG hyperspectral data [J]. Current Science. 2019, 116(7): 1172-1181.

【10】Yu J W, Cheng Z Q, Zhang J S, et al. An approach to distinguishing between species of trees and crops based on hyperspectral information [J]. Spectroscopy and Spectral Analysis. 2018, 38(12): 3890-3896.
虞佳维, 程志庆, 张劲松, 等. 高光谱信息的农林植被种类区分 [J]. 光谱学与光谱分析. 2018, 38(12): 3890-3896.

【11】Li J B, Tian X, Huang W Q, et al. Application of long-wave near infrared hyperspectral imaging for measurement of soluble solid content (SSC) in pear [J]. Food Analytical Methods. 2016, 9(11): 3087-3098.

【12】Zhang C, Guo C T, Liu F, et al. Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine [J]. Journal of Food Engineering. 2016, 179: 11-18.Zhang C, Guo C T, Liu F, et al. Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine [J]. Journal of Food Engineering. 2016, 179: 11-18.

【13】Ma T, Li X Z, Inagaki T, et al. Noncontact evaluation of soluble solids content in apples by near-infrared hyperspectral imaging [J]. Journal of Food Engineering. 2018, 224: 53-61.

【14】Rao L B, Pang T, Ji R S, et al. Firmness detection for apples based on hyperspectral imaging technology combined with stack autoencoder-extreme learning machine method [J]. Laser & Optoelectronics Progress. 2019, 56(11): 113001.
饶利波, 庞涛, 纪然仕, 等. 基于高光谱成像技术结合堆栈自动编码器-极限学习机方法的苹果硬度检测 [J]. 激光与光电子学进展. 2019, 56(11): 113001.

【15】Deng X L, Kong C, Wu W B, et al. Detection of citrus HuangLongBing based on principal component analysis and back propagation neural network [J]. Acta Photonica Sinica. 2014, 43(4): 0430002.
邓小玲, 孔晨, 吴伟斌, 等. 基于主成分分析和BP神经网络的柑橘黄龙病诊断技术 [J]. 光子学报. 2014, 43(4): 0430002.

【16】Sun Y, Gu X Z, Sun K, et al. Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches [J]. LWT-Food Science and Technology. 2017, 75: 557-564.Sun Y, Gu X Z, Sun K, et al. Hyperspectral reflectance imaging combined with chemometrics and successive projections algorithm for chilling injury classification in peaches [J]. LWT-Food Science and Technology. 2017, 75: 557-564.

【17】Fan Y Y, Qiu Z J, Chen J, et al. Identification of varieties of dried red jujubes with near-infrared hyperspectral imaging [J]. Spectroscopy and Spectral Analysis. 2017, 37(3): 836-840.
樊阳阳, 裘正军, 陈俭, 等. 基于近红外高光谱成像技术的干制红枣品种鉴别 [J]. 光谱学与光谱分析. 2017, 37(3): 836-840.

【18】Pan X Y, Sun L J, Li Y S, et al. Non-destructive classification of apple bruising time based on visible and near-infrared hyperspectral imaging [J]. Journal of the Science of Food and Agriculture. 2019, 99(4): 1709-1718.Pan X Y, Sun L J, Li Y S, et al. Non-destructive classification of apple bruising time based on visible and near-infrared hyperspectral imaging [J]. Journal of the Science of Food and Agriculture. 2019, 99(4): 1709-1718.

引用该论文

Liu Lixin,He Di,Li Mengzhu,Liu Xing,Qu Junle. Identification of Xinjiang Jujube Varieties Based on Hyperspectral Technique and Machine Learning[J]. Chinese Journal of Lasers, 2020, 47(11): 1111002

刘立新,何迪,李梦珠,刘星,屈军乐. 基于高光谱技术与机器学习的新疆红枣品种鉴别[J]. 中国激光, 2020, 47(11): 1111002

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