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基于光谱指数的蜜橘成熟度评价模型研究

刘燕德 叶灵玉 孙旭东 韩如冰 肖怀春 马奎荣 朱丹宁 吴明明

刘燕德, 叶灵玉, 孙旭东, 韩如冰, 肖怀春, 马奎荣, 朱丹宁, 吴明明. 基于光谱指数的蜜橘成熟度评价模型研究[J]. 中国光学(中英文), 2018, 11(1): 83-91. doi: 10.3788/CO.20181101.0083
引用本文: 刘燕德, 叶灵玉, 孙旭东, 韩如冰, 肖怀春, 马奎荣, 朱丹宁, 吴明明. 基于光谱指数的蜜橘成熟度评价模型研究[J]. 中国光学(中英文), 2018, 11(1): 83-91. doi: 10.3788/CO.20181101.0083
LIU Yan-de, YE Ling-yu, SUN Xu-dong, HAN Ru-bing, XIAO Huai-chun, MA Kui-rong, ZHU Dan-ning, WU Ming-ming. Maturity evaluation model of tangerine based on spectral index[J]. Chinese Optics, 2018, 11(1): 83-91. doi: 10.3788/CO.20181101.0083
Citation: LIU Yan-de, YE Ling-yu, SUN Xu-dong, HAN Ru-bing, XIAO Huai-chun, MA Kui-rong, ZHU Dan-ning, WU Ming-ming. Maturity evaluation model of tangerine based on spectral index[J]. Chinese Optics, 2018, 11(1): 83-91. doi: 10.3788/CO.20181101.0083

基于光谱指数的蜜橘成熟度评价模型研究

doi: 10.3788/CO.20181101.0083
基金项目: 

国家自然科学基金 61640417

“十二五”国家863计划课题 SS2012AA101306

江西省优势科技创新团队建设计划项目 20153BCB24002

南方山地果园智能化管理技术与装备协同创新中心 2014-60

江西省研究生创新资金项目 YC2015-S238

详细信息
    作者简介:

    刘燕德(1967—), 女, 江西泰和人, 博士, 教授, 博士生导师, 主要从事光机电检测技术方面的研究。E-mail:jxliuyd@163.com

  • 中图分类号: TS255.7

Maturity evaluation model of tangerine based on spectral index

Funds: 

National Natural Science Foundation of China 61640417

"Twelfth five-year" National 863 Plan Project SS2012AA101306

Jiangxi Advantage Science and Technology Innovation Team Construction Project 20153BCB24002

Center of the Technology and Equipment of the Intelligent Management for the Southern Mountain Orchard Collaborative Innovation 2014-60

Innovative Funds for Jiangxi Graduate Students YC2015-S238

More Information
  • 摘要: 本文探索了基于光谱指数的蜜橘成熟度快速无损评价方法及模型。以2016年9~11月份6个不同采收期的300个蜜橘作为实验样品,采集重量、横纵径、叶绿素、色差、可溶性固形物(SSC)、酸度(TA)、近红外光谱等数据。通过对比分析上述各参数的平均值和偏差,筛选出叶绿素、叶绿素/SSC、叶绿素/固酸比作为蜜橘成熟度评价指标。利用光谱变异系数分析光谱的特征,筛选出649、724、672、1 100 nm 4个特征波长,通过特征波长线性组合方法以及相关性分析,得出最佳光谱指数。接着,以225个样品为建模集、75个样品为预测集,在成熟度评价指标与光谱指数间进行多元线性回归(MLR)分析。对比发现,以叶绿素为成熟度评价指标的评价模型的预测结果最准确,建模和预测相关系数分别达到0.98和0.96,建模均方根误差(RMSEC)和预测均方根误差(RMSEP)分别为0.49和0.59,建模和预测偏差分别为-6.1×10-8和-0.014。实验结果表明,利用光谱指数能便捷、准确地评定蜜橘成熟度,为后续开发低成本测量成熟度的仪器提供了理论依据。

     

  • 图 1  便携式光谱仪

    Figure 1.  Portable spectrometer

    图 2  不同采收期的蜜橘重量

    Figure 2.  Tangerine weights in different harvest periods

    图 3  不同采收期的蜜橘横径

    Figure 3.  Transverse diameters in different harvest periods

    图 4  不同采收期的蜜橘纵径

    Figure 4.  Longitudinal diameters in different harvest periods

    图 5  不同采收期的蜜橘叶绿素

    Figure 5.  Chlorophyll in different harvest periods

    图 6  不同采收期的蜜橘色差

    Figure 6.  Chromatic aberrations in different harvest periods

    图 7  不同采收期的蜜橘可溶性固形物含量

    Figure 7.  Soluble solid contents in different harvest periods

    图 8  不同采收期的蜜橘酸度含量

    Figure 8.  Tangerine acidity contents in different harvest periods

    图 9  不同采收期的蜜橘固酸比

    Figure 9.  Tangerine solid-acid ratios in different harvest

    图 10  不同采收期的叶绿素与糖度的比值

    Figure 10.  Ratio of chlorophyll to SSC in different harvest periods

    图 11  不同采收期的叶绿素与固酸比的比值

    Figure 11.  Ratios of chlorophyll to solid-acid in different harvest periods

    图 12  不同采收期的江西蜜橘近红外光谱特性

    Figure 12.  Near infrared spectrum characteristics of Jiangxi orange in different harvest periods

    图 13  变异系数曲线

    Figure 13.  Variation coefficient curve

    图 14  成熟度光谱指数随采收期的变化

    Figure 14.  Maturity spectral index changes with the harvest period

    图 15  多元线性回归模型

    Figure 15.  Multiple linear regression model

    表  1  不同采收期的参数范围

    Table  1.   Parameter ranges of different harvest periods

    批次 采摘时间 重量/g 横径/mm 纵径/mm 叶绿素 色差 糖度(Bix°) 酸度/%
    1 9月1日 94~116.7 60~63 47~54 5.8~35 -50~-27.9 9.1~11.6 0.53~2.88
    2 9月11日 94.43~137.82 60~68 46~54 3.9~40 55.7~66.8 9.2~11.5 0.3~2.41
    3 10月9日 110.4~156.1 66~74 46~56 0.1~23.5 57.4~74.4 9.8~12.2 0.47~2.22
    4 10月24日 109.87~141.89 62~71 50~59 0.1~13.2 56.6~75.7 9.4~12.8 0.84~1.66
    5 11月8日 87.44~136.11 62~71 48~57 0.1~3.2 65.1~76.3 9.5~13.1 0.4~1.4
    6 11月28日 108.36~151.73 60~75 46~58 0.1~1.8 58.6~75.7 10.7~13.3 0.41~0.97
    下载: 导出CSV

    表  2  光谱评价指数相关性分析

    Table  2.   Correlation analysis of spectral evaluation indices

    光谱评价指数 相关系数(r)
    0.967 8
    0.955 9
    0.991 6
    -0.975 3
    0.991 2
    -0.964 3
    下载: 导出CSV

    表  3  成熟度指标与光谱指数建模结果

    Table  3.   Modeling results of maturity index and spectral indices

    成熟度指标 rc RMSEC Bias rp RMSEP Bias
    叶绿素 0.98 0.49 -6.1×10-8 0.96 0.59 -0.014
    叶绿素/SSC 0.95 0.71 3.7×10-7 0.94 0.82 -0.041
    叶绿素/固酸比 0.92 0.97 -4.1×10-8 0.89 1.02 0.084
    下载: 导出CSV
  • [1] GB/T 12947-2008《鲜柑橘》[S]. GB/T 12947-2008(Fresh citrus)[S]. (in Chinese)
    [2] 应义斌, 饶秀勤, 马俊福, 等.柑橘成熟度机器视觉无损检测方法研究[J].农业工程学报, 2004, 20(2):144-147. http://industry.wanfangdata.com.cn/dl/Detail/Periodical?id=Periodical_nygcxb200402034

    YING Y B, RAO X Q, MA J F, et al.. Methodology for nondestructive inspection of citrus maturity with machine vision[J]. Transactions of the CSAE, 2004, 20(2):144-147.(in Chinese) http://industry.wanfangdata.com.cn/dl/Detail/Periodical?id=Periodical_nygcxb200402034
    [3] BURDON J, PIDAKALA P, MARTIN P, et al. Postharvest performance of the yellow-fleshed 'Hort16A' kiwifruit in relation to fruit maturation[J]. Postharvest Biology and Technology, 2014, 92:98-106. doi: 10.1016/j.postharvbio.2014.01.004
    [4] NAVARRO G N, MARTINEZ R D, PEREZT O. Assessment of the impact of ethylene and ethylene modulators in citrus limon organogenesis[J]. Plant Cell, Tissue and Organ Culture, 2016, 127(2):405-415. doi: 10.1007/s11240-016-1062-x
    [5] 王乐妍, 张冬仙, 章海军, 等.基于激光光致发光光谱的果实成熟度测试方法研究[J].光谱学与光谱分析, 2008, 28(12):2772-2776. doi: 10.3964/j.issn.1000-0593(2008)12-2772-05

    WANG L Y, ZHANG D X, ZHANG H J, et al.. Measurement of fruit maturity based on laser-induced photoluminescence spectrum[J]. Spectroscopy and Spectral Analysis, 2008, 28(12):2772-2776.(in Chinese) doi: 10.3964/j.issn.1000-0593(2008)12-2772-05
    [6] JONATHAN V B, LAURENT T, BEN S, et al.. Stem water potential monitoring in pear orchards through worldview-2 multispectral imagery[J]. Remote Sensing, 2015, 7(8):9886-9903. http://www.academia.edu/23188991/Stem_Water_Potential_Monitoring_in_Pear_Orchards_through_WorldView-2_Multispectral_Imagery
    [7] ZHAO CH J, LI H L, GU X H, et al.. Effect of vertical distribution of crop structure and biochemical parameters of winter wheat on canopy reflectance characteristics and spectral indices[J]. IEEE Transactions on Geoscience & Remote Sensing, 2017, 55(1):236-247. https://www.researchgate.net/publication/309047816_Effect_of_Vertical_Distribution_of_Crop_Structure_and_Biochemical_Parameters_of_Winter_Wheat_on_Canopy_Reflectance_Characteristics_and_Spectral_Indices
    [8] RAYMOND E H, CRAIG S T D, LI L. Feasibility of estimating leaf water content using spectral indices from WorldView-3's near-infrared and shortwave infrared bands[J]. International Journal of Remote Sensing, 2016, 37(2):388-402. doi: 10.1080/01431161.2015.1128575
    [9] 罗丹, 常庆瑞, 齐雁冰, 等.基于光谱指数的冬小麦冠层叶绿素含量估算模型研究[J].麦类作物学报, 2016, 36(9):1225-1233. http://www.cjae.net/EN/article/downloadArticleFile.do?attachType=PDF&id=18898

    LUO D, CHANG Q R, QI Y B, et al.. Estimation model for chlorophyll content in winter wheat canopy based on spectral indices[J]. Journal of Triticeae Crops, 2016, 36(9):1225-1233.(in Chinese) http://www.cjae.net/EN/article/downloadArticleFile.do?attachType=PDF&id=18898
    [10] NAGY A, PETER R, JANOS T. Spectral evaluation of apple fruit ripening and pigment content alteration[J]. Scientia Horticulturae, 2016, 201:256-264. doi: 10.1016/j.scienta.2016.02.016
    [11] ALEJANDRA R F, MASSIMO N, EMILIO J F, et al.. Assessment of technological maturity parameters and anthocyanins in berries of cv. Sangiovese(Vitis vinifera L.) by a portable vis/NIR device[J]. Scientia Horticulturae, 2016, 209:229-235. doi: 10.1016/j.scienta.2016.06.004
    [12] 刘燕德, 肖怀春, 孙旭东, 等.基于高光谱成像的柑橘黄龙病无损检测[J].农业机械学报, 2016, 47(11):231-238, 277. doi: 10.6041/j.issn.1000-1298.2016.11.032

    LIU Y D, XIAO H CH, SUN X D, et al.. Non-destructive detection of citrus huanglong disease using hyperspectral image technique[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(11):231-238, 277.(in Chinese) doi: 10.6041/j.issn.1000-1298.2016.11.032
    [13] LI M, LV W B, ZHAO R, et al.. Non-destructive assessment of quality parameters in Friar' plums during low temperature storage using visible/near infrared spectroscopy[J]. Food Control, 2017, 73(B):1334-1341. https://www.sciencedirect.com/science/article/pii/S0956713516306107
    [14] 刘凯, 张立福, 杨杭, 等.面向对象分析的非结构化背景目标高光谱探测方法研究[J].光谱学与光谱分析, 2013, 33(6):1653-1657. http://kns.cnki.net/KCMS/detail/detail.aspx?filename=guan201306047&dbname=CJFD&dbcode=CJFQ

    LIU K, ZHANG L F, YANG H, et al.. Hyperspectral unstructured background target detection approach based on object-oriented analysis[J]. Spectroscopy and Spectral Analysis, 2013, 33(6):1653-1657.(in Chinese) http://kns.cnki.net/KCMS/detail/detail.aspx?filename=guan201306047&dbname=CJFD&dbcode=CJFQ
    [15] 田青, 罗金平, 刘晓红, 等.生物发光法细菌快速检测仪的研制及应用[J].光学精密工程, 2010, 18(4):771-778. http://www.eope.net/fileup/PDF/2009-0565.pdf

    TIAN Q, LUO J P, LIU X H, et al.. Development and application of rapid detecting instrument for bacteria based on bioluminescence[J]. Opt. Precision Eng., 2010, 18(4):771-778.(in Chinese) http://www.eope.net/fileup/PDF/2009-0565.pdf
    [16] TAMBURINI E, FERRARI G, MARCHETT M G, et al.. Development of FT-NIR models for the simultaneous estimation of chlorophyll and nitrogen content in fresh apple(Malus Domestica) leaves[J]. Sensors(Basel), 2015, 15(2):2662-2679. https://www.researchgate.net/profile/Sergio_Ferro/publication/271597970_Development_of_FT-NIR_Models_for_the_Simultaneous_Estimation_of_Chlorophyll_and_Nitrogen_Content_in_Fresh_Apple_Malus_Domestica_Leaves/links/54da02f30cf25013d043a655/Development-of-FT-NIR-Models-for-the-Simultaneous-Estimation-of-Chlorophyll-and-Nitrogen-Content-in-Fresh-Apple-Malus-Domestica-Leaves.pdf
    [17] GITELSON AA, MERZLYAK M N. Signature analysis of leaf reflectance spectra-algorithm development for remote sensing of chlorophyll[J]. Journal of Plant Physiology, 1996, 148(3-4):494-500. doi: 10.1016/S0176-1617(96)80284-7
    [18] 王智宏, 张福东, 滕飞, 等.基于近红外波长组合快速检测油页岩含油率[J].光学精密工程, 2015, 23(2):371-377. http://cdmd.cnki.com.cn/Article/CDMD-10496-1017006928.htm

    WANG ZH H, ZHANG F D, TENG F, et al.. Rapid detection of oil yield of oil shale by combination of wavelengths in near infrared spectroscopy[J]. Opt. Precision Eng., 2015, 23(2):371-377.(in Chinese) http://cdmd.cnki.com.cn/Article/CDMD-10496-1017006928.htm
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出版历程
  • 收稿日期:  2017-07-11
  • 修回日期:  2017-08-13
  • 刊出日期:  2018-02-01

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