高光谱技术无损检测单粒小麦种子生活力的特征波段筛选方法研究

doi:10.3964/j.issn.1000-0593(2019)05-1556-07

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摘要：种子活力是种子质量的一项重要指标，高活力的种子具有较强的抗逆性、生长优势及生产潜力。而种子活力在种子生理成熟时最高，随后随着贮藏时间的延长而发生着自然不可逆的降低。因此，在播种前及时、准确地对种子活力进行检测和筛选具有重要的实践意义。针对传统种子活力检测方法存在的操作过程复杂繁琐、耗时长、重复性差且对种子有破坏性等缺点，研究尝试利用高光谱成像技术建立单粒小麦种子生活力快速、无损、精确的检测方法。以高温高湿老化后的190粒小麦种子（发芽128粒，不发芽62粒）作为研究样本，先利用可见-近红外(Vis-NIR)高光谱成像系统采集样本种子的光谱图像和进行标准发芽试验，并确保光谱采集试验和标准发芽试验的小麦种子一一对应。随后提取种子光谱图像的感兴趣区域并对其光谱数据进行平均和特征分析。分别采用一阶导数（FD）、均值中心化（MC）、正交信号校正(OSC)和多元散射校正（MSC）对原始光谱数据进行预处理，结合偏最小二乘辨别分析（PLS-DA）建立全波段PLS-DA模型，比较分析，并筛选出最适预处理方法。分别利用无信息变量消除算法（UVE）、竞争性自适应重加权算法(CARS)、连续投影算法(SPA)及耦合不同变量筛选方法对特征波段进行筛选提取，再分别基于所提取出的特征波段建立PLS-DA定性判别模型，对比分析，最终确立提取与单粒小麦种子生活力相关性最高的高光谱特征波段方法体系。结果表明：不同光谱预处理建立的模型其表现有所差异，在MC，FD，OSC和MSC中，采用MC对原始高光谱数据进行预处理，建立的全波段MC-PLS-DA判别模型，其校正集和预测集对小麦种子生活力的整体鉴别正确率分别为82.5%和83.0%，优于原始及其他预处理后建立的全波段PLS-DA判别模型，其校正集和预测集对小麦种子活种子鉴别正确率分别为94.8%和90.6%。进一步对比3种单特征波段提取方法及其耦合分析建模中，发现3种变量筛选方法耦合（UVE-CARS-SPA）的方式能够将光谱全波段的688个变量压缩至8个变量（473，492，811，829，875，880，947和969 nm），利用所筛选出的8个变量建立的MC-UVE-CARS-SPA-PLS-DA模型获得了最优秀的鉴别效果，其校正集和预测集对小麦种子生活力的整体鉴别正确率分别为86.7%和85.1%，较全波段模型（MC-Full-PLS-DA）分别提升了4.2%和2.1%，活种子的鉴别正确率分别为93.8%和84.4%，经过此优秀模型筛选后，种子批最终发芽率可达到93.1%。实验结果表明，基于高光谱成像技术结合UVE-CARS-SPA-PLS-DA模型能够实现对单粒小麦种子生活力的定性判别。研究工作为小麦种子活力的快速、精确且无损的检测提供理论支持。

关键词：高光谱技术；小麦种子；生活力；检测；特征波段

Abstract：Seeds are the basis of the agricultural industry. The viability of seedsis a very important index of seed quality, which is closely related to resistance to biotic and abiotic stress, germination percentage, plant performance, and which decreases with increasing storage period.Increased understanding of wheat seed viability would be beneficial to the wheat industry by ensuring a higher yield for farmers and reducing crop variability. Seed companies would also benefit from enhanced viability by being able to ensure a higher quality product. As the viability of seeds was gradually brought to the public attention, the rapid detection of seed viability without destroying has been a research hot spot. This study aimed at investigating the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging (HSI) technique to discriminate viable and nonviable wheat seeds. Firstly, 190 wheat seeds treated by high temperature and high humidity aging (128 germination samples and 62 non-germination samples) were prepared as experimental materials. The visible and near-infrared hyperspectral imaging acquisition system （400~1 000 nm） was constructed to acquire the hyperspectral images of the wheat seeds. After HSI spectra collection of the wheat seeds, a germination test was implemented to check for seed viability. We recorded a seed as germinated (yes=1) if the plumule and radicle were both over 2 mm long, and non-germinated (no=2) if not. The average reflectance data of the region of interest were extracted for spectral characteristics analysis. Secondly, different pre-processing algorithms including the first derivative (FD), orthogonal signal correction (OSC), multiplicative scatter correction (MSC), mean centering (MC) were conducted to build partial least squares discriminant analysis (PLS-DA) model of the viability of wheat seeds. Lastly, three variable selection methods including the uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to screen the characteristic wavelengths related to seed viability. PLS-DA models were established by these characteristic wavelengths. The results showed that, the classification accuracies of different pre-processing algorithms were diverse. Among them, the MC method was the best pre-processing algorithm, from which the overall classification accuracy were 82.5% and 83.0%, and the viability classification accuracy were 94.8% and 90.6%, in calibration and prediction sets, respectively. Among the single variable selection methods, UVE method was superior to other two variable selection methods while maintaining an excellent performance of the model for overall classification accuracy (84.6%, 83.0%) and viability classification accuracy (86.5%, 78.1%) in the calibration and prediction sets. This model could promote the germination percentage of the seed lot from 67.4% to 96.2%. Comparing all variable selection methods comprehensively, the UVE-CARS-SPA method selected only 8 variables (473，492，811，829，875，880，947 and 969 nm) from the all 688 spectral variables. The PLS-DA model built by using UVE-CARS-SPA method exhibited the optimal performance with overall accuracy of 86.7% and 85.1% for calibration and prediction, respectively, and accuracy for viable seed was 93.8% and 84.4%. After screening by this model, the germination percentage of the seed lot enhanced from 67.4% to 93.1%. The results indicated that appropriate variable selection could improve the performance of a model, simplify the classification models, and increase the classification accuracy of viable and nonviable wheat seeds. In the future, combining the visible and near-infrared hyperspectral imaging technique with MC-UVE-CARS-SPA-PLS-DA can be used as a feasible and reliable method for the determination of seed viability during the storage. The result can provide the theoretical reference for rapid detection of seed viability during grain storage using spectral information.

Key words：Hyperspectral technology; Wheat seed; Viability; Detection; Characteristic wavelength

收稿日期: 2018-03-15 修订日期: 2018-07-30

中图分类号:

TP391.4

基金资助: 国家“十三五”重点研发计划(2018YFD0100904)资助

通讯作者: 孙群 E-mail: sqcau@126.com

作者简介: 张婷婷，1990年生，中国农业大学农学院博士研究生 e-mail: ztt_cau@163.com

引用本文:

张婷婷，向莹莹，杨丽明，王建华，孙群. 高光谱技术无损检测单粒小麦种子生活力的特征波段筛选方法研究[J]. 光谱学与光谱分析, 2019, 39(05): 1556-1562.
ZHANG Ting-ting, XIANG Ying-ying, YANG Li-ming, WANG Jian-hua, SUN Qun. Wavelength Variable Selection Methods for Non-Destructive Detection of the Viability of Single Wheat Kernel Based on Hyperspectral Imaging. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(05): 1556-1562.

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[1] Istrate Dan, Sîngiorzan Laura. Turkish Journal of Agriculture and Forestry, 2014, 36(4): 421.
[2] Olesen M H, Nikneshan P, Shrestha S, et al. Sensors, 2015, 15(2): 4592.
[3] Anisur R, Byoung-Kwan C. Seed Science Research, 2016, 26(4): 285.
[4] ZHANG Ting-ting, SUN Qun, YANG Lei, et al(张婷婷, 孙群, 杨磊, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(21): 275.
[5] SONG Le, WANG Qi, WANG Chun-yang, et al(宋乐, 王琦, 王纯阳, 等). Grain Storage(粮食储藏), 2015, 44(1): 20.
[6] Yang X, Hong H, You Z, et al. Sensors, 2015, 15(7): 15578.
[7] Zhang T T, Wei W S, Zhao B, et al. Sensors, 2018, 18(3): 813.
[8] Tao F, Peng Y. Journal of Food Engineering, 2014, 126(1): 98.
[9] Zhang B, Fan S, Li J, et al. Food Analytical Methods, 2015, 8(8): 2075.
[10] Wang L, Liu D, Pu H, et al. Food Analytical Methods, 2015, 8(2): 515.
[11] Gao J, Li X, Zhu F, et al. Computers and Electronics in Agriculture, 2013, 99(6): 186.
[12] XU Si, ZHAO Guang-wu, DENG Fei, et al(许思, 赵光武, 邓飞, 等). Seed(种子), 2016, 35(4): 34.
[13] Ambrose A, Kandpal L M, Kim M S, et al. Infrared Physics and Technology, 2016, 75: 173.
[14] Dai Q, Cheng J H, Sun D W, et al. Journal of Food Engineering, 2015, 149: 97.
[15] Shi T, Chen Y, Liu H, et al. Applied Spectroscopy, 2014, 68(8): 831.
[16] Baleševic-Tubic S, Tatic M, Dordevic V, et al. Romanian Biotechnological Letters, 2011, 16(6): 6740.
[17] Nansen C, Zhao G, Dakin N, et al. Journal of Photochemistry and Photobiology B Biology, 2015, 145: 19.
[18] SUN Jun, LU Xin-zi, ZHANG Xiao-dong, et al(孙俊, 路心资, 张晓东, 等). Transactions of the Chinese Society of Agricultural Machinery(农业机械学报), 2016, 47(6): 215.
[19] WEI Wen-song, PENG Yan-kun, ZHENG Xiao-chun, et al(魏文松, 彭彦昆, 郑晓春, 等). Acta Optica Sinica(光学学报), 2017, 37(11): 374.