|
|
|
|
|
|
Application of hyperspectral imaging technology in crop seeds |
PENG Xiaowei1( ), ZHANG Aijun1,2( ), WANG Nan3, ZHAO Li4 |
1. College of Resources and Environment Science, Agricultural University of Hebei,Baoding 071001, China 2. Hebei Mountain Research Institute, Baoding 071000, China 3. College of Mechanical and Electrical Engineering, Agricultural University of Hebei, Baoding 071000, China 4. College of Land and Resources, Agricultural University of Hebei, Baoding 071000, China |
|
|
Abstract Crop seeds are the most basic and original means of production in the planting industry. The selection of high-quality seeds directly determines the economic and production benefits in the agricultural production process. Hyperspectral imaging technology emerged in the 1980s, which has the characteristics of non-destruction, rapid imaging and “integration of atlas”. Previous studies of crop seeds using hyperspectral imaging technology mainly focused on the variety identification, vigor detection, and seed quality of crop seeds. In this paper, based on the previous research, the authors summarize and refine the data processing models, which include such methods as partial least square method, Ada-Boost algorithm, limit learning machine (ELM), random forest (RF), support vector machine (SVM), and artificial neural network (ANN). To sum up, the purpose of this paper is to provide the best spectral range, sample types, noise reduction methods, feature band extraction, model building and other aspects as the basis for various types of crop seed research, and to provide suggestions for future research direction.
|
Keywords
spectral imaging technology
crop seeds
characteristic band
detection
|
|
Corresponding Authors:
ZHANG Aijun
E-mail: 1187846870@qq.com;xm70526@163.com
|
Issue Date: 23 December 2020
|
|
|
[1] |
丁冬. 高光谱成像技术及其在农产品检测中的应用[J]. 科技信息, 2013(35):72,98.
|
[1] |
Ding D. Hyperspectral imaging technology and its application in agricultural product detection[J]. Science and Technology Information, 2013(35):72,98.
|
[2] |
Quan J G, Bai B, Jin S, et al. Indoor positioning modeling by visible light communication and imaging[J]. Chinese Optics Letters, 2014,12(5):61-64.
|
[3] |
Dana W, Ivo W. Computer image analysis of seed shape and seed color for flax cultivar description[J]. Computers and Electronics in Agriculture, 2008,61(2):126-135.
doi: 10.1016/j.compag.2007.10.001
url: https://linkinghub.elsevier.com/retrieve/pii/S0168169907002189
|
[4] |
章涛, 于雷, 易军, 等. 高光谱小波能量特征估测土壤有机质含量[J]. 光谱学与光谱分析, 2019,39(10):3217-3222.
|
[4] |
Zhang T, Yu L, Yi J, et al. Determination of soil organic matter content based on hyperspectral wavelet energy features[J]. Spectroscopy and Spectral Analysis, 2019,39(10):3217-3222.
|
[5] |
李天胜, 崔静, 王海江, 等. 基于高光谱特征波长的冬小麦水分含量估测模型[J]. 新疆农业科学, 2019,56(10):1772-1782.
|
[5] |
Li T S, Cui J, Wang H J, et al. Study on water content estimation model of winter wheat based on hyperspectral characteristic wavelength[J]. Xinjiang Agricultural Sciences, 2019,56(10):1772-1782.
|
[6] |
彭一平, 刘振华, 王璐, 等. 华南地区土壤全钾含量高光谱反演模型研究[J]. 西南农业学报, 2019,32(10):2383-2389.
|
[6] |
Peng Y P, Liu Z H, Wang L, et al. Hyperspectral inversion model of soil total potassium content in south China[J]. Southwest China Journal of Agricultural Sciences, 2019,32(10):2383-2389.
|
[7] |
张影, 赵小娟, 王迪. 基于高光谱遥感的农作物分类研究进展[J]. 中国农业信息, 2019,31(5):1-12.
|
[7] |
Zhang Y, Zhao X J, Wang D. Research advances on crop identification using hyperspectral remote sensing[J]. China Agricultural Information, 2019,31(5):1-12.
|
[8] |
程雪, 贺炳彦, 黄耀欢, 等. 基于无人机高光谱数据的玉米叶面积指数估算[J]. 遥感技术与应用, 2019,34(4):775-784.
|
[8] |
Cheng X, He B Y, Hang Y H, et al. Estimation of corn leaf area index based on UAV hyperspectral image[J]. Remote Sensing Technology and Application, 2019,34(4):775-784.
|
[9] |
白青蒙, 韩玉国, 彭致功, 等. 利用叶面积指数优化冬小麦高光谱水分预测模型[J]. 应用与环境生物学报, 2020,26(4):1-11.
|
[9] |
Bai Q M, Han Y G, Peng Z G, et al. Optimizing winter wheat hyperspectral moisture prediction model using leaf area index[J]. Chinese Journal of Applied and Environmental Biology, 2020,26(4):1-11.
|
[10] |
张航, 姚传安, 蒋梦梦, 等. 基于高光谱图像技术的小麦种子分类识别研究[J]. 麦类作物学报, 2019,39(1):96-104.
|
[10] |
Zhang H, Yao C A, Jiang M M, et al. Research on wheat seed classification and recognition based on hyperspectral imaging[J]. Journal of Triticeae Crops, 2019,39(1):96-104.
|
[11] |
陈泽贤, 袁辉. 种子活力测定方法研究进展[J]. 种子科技, 2019,37(16):25-27.
|
[11] |
Chen Z X, Yuan H. Research progress of seed vigor test methods[J]. Seed Science and Technology, 2019,37(16):25-27.
|
[12] |
Shrestha S, Deleuran L C, Olesen M H, et al. Use of multispectral imaging in varietal identification of Tomato[J]. Sensors, 2015,15(2):4496-4512.
pmid: 25690549
url: https://www.ncbi.nlm.nih.gov/pubmed/25690549
|
[13] |
Rahman A, Cho B K. Assessment of seed quality using non-destructive measurement techniques:A review[J]. Seed Science Research, 2016,26(4):285-305.
doi: 10.1017/S0960258516000234
url: https://www.cambridge.org/core/product/identifier/S0960258516000234/type/journal_article
|
[14] |
张婷婷, 孙群, 杨磊, 等. 基于电子鼻传感器阵列优化的甜玉米种子活力检测[J]. 农业工程学报, 2017,33(21):275-281.
|
[14] |
Zhang T T, Sun Q, Yang L, et al. Vigor detection of sweet corn seeds by optimal sensor array based on electronic nose[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017,33(21):275-281.
|
[15] |
宋乐, 王琦, 王纯阳, 等, 基于近红外光谱的单粒水稻种子活力快速无损检测[J]. 粮食储藏, 2015,44(1):20-23.
|
[15] |
Song L, Wang Q, Wang C Y, et al. Qualitative analysis of single rice seed vigor using near infrared reflectance spectroscopy[J]. Grain Storage, 2015,44(1):20-23.
|
[16] |
Miralbés C. Discrimination of European wheat varieties using near infrared reflectance spectroscopy[J]. Food Chemistry, 2008,106(1):386-389.
|
[17] |
黄敏, 朱晓, 朱启兵, 等. 基于高光谱图像的玉米种子特征提取与识别[J]. 光子学报, 2012,41(7):868-873.
|
[17] |
Huang M, Zhu X, Zhu Q B, et al. Morphological characteristics of maize seed extraction and identification based on the hyperspectral image[J]. Acta Photonica Sinica, 2012,41(7):868-873.
|
[18] |
吴翔, 张卫正, 陆江锋, 等. 基于高光谱技术的玉米种子可视化鉴别研究[J]. 光谱学与光谱分析, 2016,36(2):511-514.
pmid: 27209759
|
[18] |
Wu X, Zhang W Z, Lu J F, et al. Study on visual identification of corn seeds based on hyperspectral imaging technology[J]. Spectroscopy and Spectral Analysis, 2016,36(2):511-514.
pmid: 27209759
url: https://www.ncbi.nlm.nih.gov/pubmed/27209759
|
[19] |
魏利峰. 玉米种子高光谱图像品种检测方法研究[D]. 沈阳:沈阳农业大学, 2017.
|
[19] |
Wei L F. Research on detection method of maize variety based on hyperspectral image[D]. Shenyang:Shenyang Agricultural University, 2017.
|
[20] |
王庆国, 黄敏, 朱启兵, 等. 基于高光谱图像的玉米种子产地与年份鉴别[J]. 食品与生物技术学报, 2014,33(2):163-170.
|
[20] |
Wang Q G, Huang M, Zhu Q B, et al. Geographical origin and years identification of maize seeds based on the hyperspectral image[J]. Journal of Food Science and Biotechnology, 2014,33(2):163-170.
|
[21] |
邓小琴, 朱启兵, 黄敏. 融合光谱、纹理及形态特征的水稻种子品种高光谱图像单粒鉴别[J]. 激光与光电子学进展, 2015,52(2):128-134.
|
[21] |
Deng X Q, Zhu Q B, Huang M. Variety discrimination for single rice seed by integrating spectral,texture and morphological features based on hyperspectral image[J]. Laser and Optoelectronics Progress, 2015,52(2):128-134.
|
[22] |
彭丽君. 水稻种子鉴别的近红外光谱快速无损分析[D]. 广州:暨南大学, 2018.
|
[22] |
Peng L J. Rapid and nondestructive analysis for the identification of rice seeds with near infrared spectroscopy[D]. Guangzhou:Jinan University, 2018.
|
[23] |
高海龙, 李小昱, 徐森淼, 等. 马铃薯黑心病和单薯质量的透射高光谱检测方法[J]. 农业工程学报, 2013,29(15):279-285.
|
[23] |
Gao H L, Li X Y, Xu S M, et al. Transmission hyperspectral detection method for weight and black heart of potato[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013,29(15):279-285.
url: http://www.tcsae.org/nygcxb/ch/reader/view_abstract.aspx?file_no=20131534&flag=1
|
[24] |
吴龙国, 何建国, 刘贵珊, 等. 基于近红外高光谱成像技术的长枣含水量无损检测[J]. 光电子·激光, 2014,25(1):135-140.
|
[24] |
Wu L G, He J G, Liu G S, et al. Non-destructive determination of moisture in jujubes based on near-infrared hyperspectral imaging technique[J]. Journal of Optoelectronics·Laser, 2014,25(1):135-140.
|
[25] |
丁秋. 基于高光谱成像技术小麦籽粒品种鉴别研究[D]. 武汉:武汉轻工大学, 2017.
|
[25] |
Ding Q. Studies on varieties identification of wheat grain based on hyperspectral imaging technique[D]. Wuhan:Wuhan Polytechnic University, 2017.
|
[26] |
李晓丽, 唐月明, 何勇, 等. 基于可见/近红外光谱的水稻品种快速鉴别研究[J]. 光谱学与光谱分析, 2008,28(3):578-581.
pmid: 18536416
|
[26] |
Li X L, Tang Y M, He Y, et al. Discrimination of varieties of paddy based on VIS/NIR spectroscopy combined with chemometrics[J]. Spectroscopy and Spectral Analysis, 2008,28(3):578-581.
pmid: 18536416
url: https://www.ncbi.nlm.nih.gov/pubmed/18536416
|
[27] |
柯梽全, 王阳恩, 范润洲, 等. 基于激光诱导击穿光谱的水稻品种鉴别研究[J]. 激光杂志, 2016,37(9):56-60.
|
[27] |
Ke Z Q, Wang Y E, Fan R Z, et al. Identification of rice seed varieties based on LIBS[J]. Laser Journal, 2016,37(9):56-60.
|
[28] |
Kong W, Zhang C, Liu F, et al. Rice seed cultivar identification using near-infrared hyperspectral imaging and multivariate data analysis[J]. Sensors, 2013,13(7):8916-8927.
doi: 10.3390/s130708916
pmid: 23857260
url: https://www.ncbi.nlm.nih.gov/pubmed/23857260
|
[29] |
张初, 刘飞, 孔汶汶, 等. 利用近红外高光谱图像技术快速鉴别西瓜种子品种[J]. 农业工程学报, 2013,29(20):270-277.
url: http://www.tcsae.org/nygcxb/ch/reader/view_abstract.aspx?file_no=20132035&flag=1
|
[29] |
Zhang C, Liu F, Gong W W, et al. Fast identification of watermelon seed variety using near infrared hyperspectral imaging technology[J]. Transactions of the Chinese Society of Agricultural Engineering, 2013,29(20):270-277.
url: http://www.tcsae.org/nygcxb/ch/reader/view_abstract.aspx?file_no=20132035&flag=1
|
[30] |
邹伟, 方慧, 刘飞, 等. 基于高光谱图像技术的油菜籽品种鉴别方法研究[J]. 浙江大学学报(农业与生命科学版), 2011,37(2):175-180.
url: http://www.journals.zju.edu.cn/agr/CN/abstract/abstract9871.shtml
|
[30] |
Zou W, Fang H, Liu F, et al. Identification of rapeseed varieties based on hyperspectral imagery[J]. Journal of Zhejiang University(Agriculture and Life Sciences), 2011,37(2):175-180.
|
[31] |
Tan K, Chai Y, Song W, et al. Identification of soybean seed varieties based on hyperspectral image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014,30(9):235-242.
url: http://www.tcsae.org/nygcxb/ch/reader/view_abstract.aspx?file_no=20140929&flag=1
|
[32] |
Hashim H, Osman F N, Junid SAMA, et al. An intelligent classification model for rubber seed clones based on shape features through imaging techniques[C]// International Conference on Intelligent Systems, 2010:25-31.
|
[33] |
程术希, 孔汶汶, 张初, 等. 高光谱与机器学习相结合的大白菜种子品种鉴别研究[J]. 光谱学与光谱分析, 2014,34(9):2519-2522.
doi: 10.3964/j.issn.1000-0593(2014)09-2519-04
pmid: 25532356
url: http://dx.doi.org/10.3964/j.issn.1000-0593(2014)09-2519-04
|
[33] |
Cheng S X, Kong W W, Zhang C, et al. Variety recognition of chinese cabbage seeds by hyperspectral imaging combined with machine learning[J]. Spectroscopy and Spectral Analysis, 2014,34(9):2519-2522.
pmid: 25532356
url: https://www.ncbi.nlm.nih.gov/pubmed/25532356
|
[34] |
Mo C, Kim G, Lee K, et al. Non-destructive quality evaluation of pepper (Capsicum annuum L.) seeds using LED-induced hyperspectral reflectance imaging[J]. Sensors, 2014,14(4):7489.
doi: 10.3390/s140407489
pmid: 24763251
url: https://www.ncbi.nlm.nih.gov/pubmed/24763251
|
[35] |
Mo C, Kim M S, Lim J. Multispectral fluorescence imaging technique for discrimination of cucumber seed viability[J]. Transactions of the American Society of Agricultural and Biological Engineers, 2015,58(4):959-968.
|
[36] |
张婷婷, 向莹莹, 杨丽明, 等. 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(5):1556-1562.
|
[36] |
Zhang T T, Xiang Y Y, Yang L M, et al. Wavelength variable selection methods for non-destructive detection of the viability of single wheat kernel based on hyperspectral imaging[J]. Spectroscopy and Spectral Analysis, 2019,39(5):1556-1562.
|
[37] |
Tigabu M, Oden P C. Discrimination of viable and empty seeds of Pinus patula Schiede & Deppe with near-infrared spectroscopy[J]. New Forests, 2003,25(3):163-176.
|
[38] |
李美凌, 邓飞, 刘颖, 等. 基于高光谱图像的水稻种子活力检测技术研究[J]. 浙江农业学报, 2015,27(1):1-6.
|
[38] |
Li M L, Deng F, Liu Y, et al. Study on detection technology of rice seed vigor based on hyperspectral image[J]. Acta Agriculturae Zhejiangensis, 2015,27(1):1-6.
|
[39] |
尤佳. 基于高光谱图像的脱绒棉种活力检测方法研究[D]. 石河子:石河子大学, 2017.
|
[39] |
You J. The detection method research on delinted cottonseeds’ vigor based on hyperspectral imaging[D]. Shihezi:Shihezi University, 2017.
|
[40] |
黄蒂云. 基于高光谱图像技术的脱绒棉种品种鉴别方法研究[D]. 石河子:石河子大学, 2018.
|
[40] |
Hang D Y. Study on identification method of delinted cottonseeds varieties based on hyperspectral image technology[D]. Shihezi:Shihezi University, 2018.
|
[41] |
Soltani A, Lestander T, Tigabu M, et al. Prediction of viability of oriental beechnuts,fagus orientalis,using near infrared spectroscopy and partial least squares regression[J]. Journal of Near Infrared Spectroscopy, 2003,11(1):357.
|
[42] |
Ambrose A, Kandpal L M, Kim M S, et al. High speed measurement of corn seed viability using hyperspectral imaging[J]. Infrared Physics and Technology, 2016,75:173-179.
|
[43] |
Zhang T, Wei W, Zhao B, et al. A reliable methodology for determining seed viability by using hyperspectral data from two sides of wheat seeds[J]. Sensors, 2018,18(3):813.
|
[44] |
McGoverin C M, Engelbrecht P, Geladi P, et al. Characterisation of non-viable whole barley,wheat and sorghum grains using near-infrared hyperspectral data and chemometrics[J]. Analytical and Bioanalytical Chemistry, 2011,401(7):2283-2289.
doi: 10.1007/s00216-011-5291-x
pmid: 21842198
url: https://www.ncbi.nlm.nih.gov/pubmed/21842198
|
[45] |
Kandpal L M, Lohumi S, Kim M S, et al. Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds[J]. Sensors and Actuators B Chemical, 2016,229:534-544.
|
[46] |
杨冬风, 尹淑欣, 姜丽, 等. 玉米种子活力近红外光谱智能检测方法研究[J]. 核农学报, 2013,27(7):957-961.
|
[46] |
Yang D F, Yin S X, Jiang L, et al. Research on maize vigor intelligent detection based on near infrared spectroscopy[J]. Journal of Nuclear Agricultural Sciences, 2013,27(7):957-961.
|
[47] |
Wakholi C, Kandpal L M, Lee H, et al. Rapid assessment of corn seed viability using short wave infrared line-scan hyperspectral imaging and chemometrics[J]. Sensors and Actuators B Chemical, 2018,255:498-507.
|
[48] |
Ambrose A, Lohumi S, Lee W H, et al. Comparative nondestructive measurement of corn seed viability using Fourier transform near-infrared (FT-NIR) and Raman spectroscopy[J]. Sensors and Actuators B Chemical, 2016,224:500-506.
|
[49] |
许思, 赵光武, 邓飞, 等. 基于高光谱的水稻种子活力无损分级检测[J]. 种子, 2016,35(4):34-40.
|
[49] |
Xu S, Zhao G W, Deng F, et al. Research on detection technology of rice seed vigor based on hyperspectral[J]. Seed, 2016,35(4):34-40.
|
[50] |
吴小芬, 赵光武, 祁亨年. 高光谱技术在常规水稻种子活力检测中的应用[J]. 安徽农业科学, 2017,45(29):12-14.
|
[50] |
Wu X F, Zhao G W, Qi H N. Inspect rice seed vigor of conventional rice by hyperspectral imaging with chemometric methods[J]. Journal of Anhui Agricultural Sciences, 2017,45(29):12-14.
|
[51] |
Wu D, Sun D W. Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment:A review — Part II:Applications[J]. Innovative Food Science and Emerging Technologies, 2013,19(1):1-14.
|
[52] |
Dale L M, Thewis A, Boudry C, et al. Hyperspectral imaging applications in agriculture and agro-food product quality and safety control:A review[J]. Applied Spectroscopy Reviews, 2013,48(2):142-159.
|
[53] |
Norris K H, Barnes R, Moore J E, et al. Predicting forage quality by infrared replectance spectroscopy[J]. Journal of Animal Science, 1976,43:889.
doi: 10.2527/jas1976.434889x
url: https://academic.oup.com/jas/article/43/4/889-897/4697632
|
[54] |
Wallays C, Missotten B, Baerdemaeker J D, et al. Hyperspectral waveband selection for on-line measurement of grain cleanness[J]. Biosystems Engineering, 2009,104(1):1-7.
doi: 10.1016/j.biosystemseng.2009.05.011
url: https://linkinghub.elsevier.com/retrieve/pii/S1537511009001792
|
[55] |
Agelet L. Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels[J]. Journal of Cereal Science, 2012,55(2):160-165.
doi: 10.1016/j.jcs.2011.11.002
url: http://dx.doi.org/10.1016/j.jcs.2011.11.002
|
[56] |
Kiratiratanapruk K, Sinthupinyo W. Color and texture for corn seed classification by machine vision[C]// International Symposium on Intelligent Signal Processing and Communications Systems, 2012:1-5.
|
[57] |
王超鹏, 黄文倩, 樊书祥, 等. 基于高光谱成像技术与CARS算法的玉米种子含水率检测[J]. 激光与光电子学进展, 2016,53(12):260-267.
|
[57] |
Wang C P, Huang W Q, Fan S X, et al. Moisture content detection of maize kernels based on hyperspectral imaging technology and CARS[J]. Laser and Optoelectronics Progress, 2016,53(12):260-267.
|
[58] |
Singh C B, Jayas D S, Paliwal J, et al. Detection of sprouted and midge-damaged wheat kernels using near-infrared hyperspectral imaging[J]. Cereal Chemistry, 2009,86(3):256-260.
|
[59] |
Singh C B, Jayas D S, Paliwal J, et al. Fungal damage detection in wheat using short-wave near-infrared hyperspectral and digital colour imaging[J]. International Journal of Food Properties, 2012,15(1):11-24.
|
[60] |
Williams P, Manley M, Fox G, et al. Indirect detection of Fusarium verticillioides in maize (Zea maize L.) kernels by NIR hyperspectral imaging[J]. Journal of Near Infrared Spectroscopy, 2009,18(1):49-58.
|
[61] |
Xing J, Stephen S, Muhammad S, et al. Detection of sprout damage in Canada Western Red Spring wheat with multiple wavebands using visible/near-infrared hyperspectral imaging[J]. Biosystems Engineering, 2010,106(2):188-194.
doi: 10.1016/j.biosystemseng.2010.03.010
url: https://linkinghub.elsevier.com/retrieve/pii/S1537511010000620
|
[62] |
Del Fiore S, Reverberi M, Ricelli A, et al. Early detection of toxigenic fungi on maize by hyperspectral imaging analysis[J]. International Journal of Food Microbiology, 2010,144(1):64-71.
doi: 10.1016/j.ijfoodmicro.2010.08.001
pmid: 20869132
url: https://www.ncbi.nlm.nih.gov/pubmed/20869132
|
[63] |
Yao H, Hruska Z, Kincaid R, et al. Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores[J]. Food Additives and Contaminants, 2010,27(5):701-709.
doi: 10.1080/19440040903527368
pmid: 20221935
url: https://www.ncbi.nlm.nih.gov/pubmed/20221935
|
[64] |
袁莹, 王伟, 褚璇, 等. 基于高光谱成像技术和因子判别分析的玉米黄曲霉毒素检测研究[J]. 中国粮油学报, 2014,29(12):107-110.
|
[64] |
Yuan Y, Wang W, Zhu X, et al. Detection of corn aflatoxin based on hyperspectral imaging technology and factor discriminant analysis[J]. Journal of the Chinese Cereals and Oils Association, 2014,29(12):107-110.
|
[65] |
Shahin M A, Symons S J. Detection of Fusarium damaged kernels in Canada Western Red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis[J]. Computers and Electronics in Agriculture, 2011,75(1):107-112.
|
[66] |
Barbedo J G A, Tibola C S, Fernandes J M C. Detecting Fusarium head blight in wheat kernels using hyperspectral imaging[J]. Biosystems Engineering, 2015,131:65-76.
|
[67] |
Velasco L, Möllers C. Nondestructive assessment of protein content in single seeds of rapeseed (Brassica napus L.) by near-infrared reflectance spectroscopy[J]. Euphytica, 2002,123(1):89-93.
|
[68] |
Cogdill R P, Hurburgh Jr C R, Rippke G R, et al. Single-kernel maize analysis by near-infrared hyperspectral imaging[J]. Transaction of the ASAE, 2004,47(1):311-320.
|
[69] |
Weinstock B A, Janni J, Hagen L, et al. Prediction of oil and oleic acid concentrations in individual corn (Zea mays L.) kernels using near-infrared reflectance hyperspectral imaging and multivariate analysis[J]. Applied Spectroscopy, 2006,60(1):9.
doi: 10.1366/000370206775382631
pmid: 16454902
url: https://www.ncbi.nlm.nih.gov/pubmed/16454902
|
[70] |
Font R, Río-Celestino M D, Haro-Bailón A D. The use of near-infrared spectroscopy (NIRS) in the study of seed quality components in plant breeding programs[J]. Industrial Crops and Products, 2006,24(3):307-313.
|
[71] |
Tallada J G, Palacios-Rojas N, Armstrong P R. Prediction of maize seed attributes using a rapid single kernel near infrared instrument[J]. Journal of Cereal Science, 2009,50(3):381-387.
|
[72] |
翟亚锋, 苏谦, 邬文锦, 等. 基于仿生模式识别和近红外光谱的转基因小麦快速鉴别方法[J]. 光谱学与光谱分析, 2010,30(4):924-928.
pmid: 20545132
|
[72] |
Zhai Y F, Su Q, Wu W J, et al. Fast discrimination of varieties of transgene wheat based on biomimetic pattern recognition and near infrared spectra[J]. Spectroscopy and Spectral Analysis, 2010,30(4):924-928.
pmid: 20545132
url: https://www.ncbi.nlm.nih.gov/pubmed/20545132
|
[73] |
Feng X, Zhao Y, Zhang C, et al. Discrimination of transgenic maize kernel using NIR hyperspectral imaging and multivariate data analysis[J]. Sensors, 2017,17(8):1894.
|
[74] |
林萍, 高明清, 陈永明. 基于近红外光谱分析技术的转Bt基因水稻种子及其亲本快速鉴别方法[J]. 江苏农业科学, 2019,47(13):72-75.
|
[74] |
Lin P, Gao M Q, Chen Y M. Near-infrared spectroscopy analysis technique for rapid identification of Bt transgenic rice seeds and their parents[J]. Jiangsu Agricultural Sciences, 2019,47(13):72-75.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|