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作物学报 ›› 2010, Vol. 36 ›› Issue (11): 1981-1989.doi: 10.3724/SP.J.1006.2010.01981

• 研究简报 • 上一篇    下一篇

利用数码相机和成像光谱仪估测棉花叶片叶绿素和氮素含量

王方永1,王克如1,2,李少昆1,2,*,陈兵1,陈江鲁1   

  1. 1 新疆兵团绿洲生态农业重点开放实验室 / 新疆作物高产研究中心, 新疆石河子 832003；2 中国农业科学院作物科学研究所 / 农业部作物生理生态与栽培重点开放实验室, 北京 100081
  • 收稿日期:2010-03-22 修回日期:2010-06-27 出版日期:2010-11-12 网络出版日期:2010-08-30
  • 通讯作者: 李少昆, E-mail: Lishk@mail.caas.net.cn; Tel:010-82108891
  • 基金资助:

    本研究由国家高技术研究发展计划(863计划)项目(2006AA10Z207, 2006AA10A302)和国家自然科学基金项目(30860139)资助。

Estimation of Chlorophyll and Nitrogen Contents in Cotton Leaves Using Digital Camera and Imaging Spectrometer

WANG Fang-Yong1,WANG Ke-Ru1,2,LI Shao-Kun1,2,*,CHEN Bing1,CHEN Jiang-Lu1   

  1. 1 Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops / The Center of Crop High-yield Research, Shihezi 832003, China; 2 Institute of Crop Sciences, Chinese Academy of Agriculture Sciences / Key Laboratory of Crop Physiology and Production,  Ministry of Agriculture, Beijing 100081, China
  • Received:2010-03-22 Revised:2010-06-27 Published:2010-11-12 Published online:2010-08-30
  • Contact: LI Shao-Kun,E-mail:Lishk@mail.caas.net.cn;Tel:010-82108891

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摘要： 实时、无损监测棉花叶片的叶绿素和氮素含量对诊断棉花生理状况和氮肥精确管理具有重要意义。本研究基于MSI200成像光谱仪和数码相机两种可见光传感器，分析和比较了光谱和颜色参数与叶绿素、氮素浓度和SPAD读数的关系，并且确立了其定量预测模型。结果表明，不同传感器对叶绿素和氮素最敏感的波段分别为R710和R；光谱指数与叶绿素、氮素浓度和SPAD读数的相关性比原始光谱好，而且以蓝光和红光波段组成的差值指数(DI和R–B)的预测能力最佳；DI所建棉花叶片Chl a+b、Chl a、Chl b、N和SPAD读数的预测模型的预测误差分别为0.0058、0.0050、0.0018和2.3002 mg g–1和4.9736(分别为均值的18.39%、19.47%、30.33%、11.69%和8.45%)，预测精度R2分别为0.7965、0.7582、0.6608、0.7019和0.7338；R–B所建模型的预测性比DI差，对Chl a+b的预测精度最高(R2=0.7400)，而预测Chl b的精度最低(R2=0.5653)。基于CIE 1976 L*a*b*颜色模型的颜色参数b*和HSI颜色模型的S是两种传感器与叶绿素、氮素浓度和叶色关系较好的颜色参数；b*对叶绿素、氮素浓度和SPAD读数的预测能力稍逊于DI，预测误差和精度都与DI的比较接近；而饱和度S值的预测RRMSE最大，整体预测精度小于0.62。因此，可以利用可见光成像传感器的光谱和颜色参数估测棉花叶片叶绿素和氮素含量。

关键词: 棉花叶片, 光谱指数, 颜色参数, 叶绿素, 氮素, SPAD读数

Abstract: Leaf chlorophyll and nitrogen concentrations of cotton (Gossypium hirsutum L.) are important indicators of plant N status. They can provide valuable insights into the physiological performance of leaves. The objectives of this study were to determine the relationships between chlorophyll, nitrogen, SPAD readings and leaf spectral and color parameters in cotton. Spectral and color parameters for the non-destructive estimation of chlorophyll, nitrogen contents and SPAD readings were obtained by using digital camera (Olympus C-5060) and imaging spectrometer (MSI200) so a wide range of them was established in cotton. The dataset was separated into two parts using for calibration (n=100) and validation (n=60), respectively. Therefore, a systematic analysis was undertaken on quantitative relationships of chlorophyll, nitrogen, SPAD readings to major spectral indices, such as the ratio index (RI), normalized difference index (ND) and difference index (DI), composed of any two wavelengths with original reflectance and color parameters. The results indicated that the maximum sensitivity of reflectance to variation in chlorophyll, nitrogen contents and SPAD readings was found in the far-red wavelength region at 710 nm and in the red wavelength region (R) for two sensors, respectively. Furthermore, spectral indices could improve the prediction ability obviously, and difference indices (DI and R-B) of different sensors composed of blue and red wavelengths gave a better prediction performance. The models to retrieve chlorophyll, nitrogen contents and SPAD readings using DI were the most feasible models with the maximum determination coefficients (R2) and the minimum RMSE, especially, DI (R440, R710), DI (R440, R710), DI (R420, R710), DI (R420, R720) and DI (R490, R710) were the optimum indices for the models of chlorophyll a+b, chlorophyll a, chlorophyll b and N, and SPAD readings, respectively. R-B was the optimum index of digital camera but its prediction performances were lower than these of DI. Additional, b* (CIE 1976 L*a*b* color model) and S (HSI color model) were the optimum color parameters, and the prediction ability of b* was lower than that of DI. However, the prediction performance of S was relative weak with the highest RRMSE and the lowest R2. Thus, measurements of leaf reflectance and color in visible range by using digital camera and imaging spectrometer may provide a real-time and accurate means of estimating leaf chlorophyll and nitrogen contents and monitoring of cotton plant nitrogen status and N fertilizer management in the field.

Key words: Cotton leaves, Spectral index, Color parameter, Chlorophyll, Nitrogen, SPAD readings

[1]Gitelson A A, Gritz Y, Merzlyak M N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol, 2003, 160: 271–282
[2]Filella I, Serrano L, Serra J, Peńuelas J. Evaluating wheat nitrogen status with canopy re?ectance indices and discriminant analysis. Crop Sci, 1995, 35: 1400–1405
[3]Blackburn G A. Spectral indices for estimating photosynthetic pigment concentrations: a test using senescent tree leaves. Intl J Remote Sens, 1998, 19: 657–675
[4]Moran J A, Mitchell A K, Goodmanson G, Stockburger K A. Differentiation among effects of nitrogen fertilization treatments on conifer seedlings by foliar re?ectance: a comparison of methods. Tree Physiol, 2000, 20: 1113–1120
[5]Schepers J S, Blackmer T M, Wilhelm W W, Resende M. Transmittance and reflectance measurements of corn leaves from plants with different nitrogen and water supply. J Plant Physiol, 1996, 148: 523–529
[6]Fang Z, Bouwkamp J, Solomos T. Chlorophyllase activities and chlorophyll degradation during leaf senescence in non-yellowing mutant and wild type of Phaseolus vulgaris L. J Exp Bot, 1998, 49: 503–510
[7]Buscaglia H J, Varco J J. Early detection of cotton leaf nitrogen status using leaf reflectance. J Plant Nutr, 2002, 25: 2067–2080
[8]Kawashima S, Nakatani M. An algorithm forestimating chlorophyll content in leaves using a video camera. Ann Bot, 1998, 81: 49–54
[9]Thomas J R, Gausman H W. Leaf reflectance vs leaf chlorophyll and carotenoid concentration for eight crops. Agron J, 1977, 69: 799–802
[10]Madeira A C, Ferreira A, Varennes A, Vieira M I. SPAD meter versus tristimulus colorimeter to estimate chlorophyll content and leaf color in sweet pepper. Commun Soil Sci Plant Anal, 2003, 34: 2461–2470
[11]Xue L, Yang L. Deriving leaf chlorophyll content of green-leafy vegetables from hyperspectral reflectance. ISPRS J Photogrammetry Remote Sens, 2008, doi:10.1016/j.isprsjprs. 2008.06.002
[12]Schlemmer M R, Francis D D, Shanahan J F, Schepers J S. Remotely measuring chlorophyll content in corn leaves with differing nitrogen levels and relative water content. Agron J, 2005, 97: 106–112
[13]Datt B. Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a+b, and total carotenoid content in Eucalyptus leaves. Remote Sens Environ, 1998, 66: 111–121
[14]Blackmer T M, Schepers J S, Varvel G E. Light reflectance compared with other nitrogen stress measurements in corn leaves. Agron J, 1994, 86: 934–938
[15]Blackmer T M, Schepers J S, Varvel G E, Walter-Shea E A. Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agron J, 1994, 88: 1–5
[16]Xue L, Cao W, Luo W, Dai T, Zhu Y. Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agron J, 2004, 96: 135–142
[17]Zhao D, Reddy K R, Kakani V G, Read J J, Koti S. Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton. Agron J, 2005, 97: 89–98
[18]Jin Z-Y(金震宇), Tian Q-J(田庆久), Hui F-M(惠凤鸣), Lu J-F(陆建飞). Study of the relationship between rice chlorophyll concentration and rice reflectance. Remote Sens Technol Appl (遥感技术与应用), 2003, 18(3): 134–137 (in Chinese with English abstract)
[19]Tang Y-L(唐延林), Huang J-F(黄敬峰), Wang R-C(王人潮). Change law of hyperspectral data with chlorophyll and carotenoid for rice at different developmental stages. Chin J Rice Sci (中国水稻科学), 2004, 18(1): 59–66 (in Chinese with English abstract)
[20]Gonzalez R C, Woods R E. Digital Image Processing, Second Edition. Pearson Education, Inc., publishing as Prentice Hall, 2002. pp 4–10
[21]Pu R-L(浦瑞良), Gong P(宫鹏). Hyperspectral Remote Sensing and Its Application (高光谱遥感及其应用). Beijing: Higher Education Press, 2000. p 22 (in Chinese)
[22]Noh H, Zhang Q, Shin B, Han S, Feng L. A neural network model of maize crop nitrogen stress assessment for a multi-spectral imaging sensor. Biosyst Eng, 2006, 94: 477–485
[23]Jia L, Buerkert A, Chen X, Roemheld V, Zhang F. Low-altitude aerial photography for optimum N fertilization of winter wheat on the North China Plain. Field Crops Res, 2004, 89: 389–395
[24]Jia L, Chen X, Zhang F, Buerkert A, Römheld V. Use of digital camera to assess nitrogen status of winter wheat in the northern China plain. J Plant Nutr, 2004, 27: 441–450
[25]Graeff, S, Claupein W. Quantifying nitrogen status of corn (Zea mays L.) in the field by reflectance measurements. Eur J Agron, 2003, 19: 611–618
[26]Graeff S, Pfenning J, Claupein W, Liebig H P. Evaluation of image analysis to determine the N-fertilizer demand of Broccoli plants (Brassica oleracea convar. botrytis var. italica). Adv Optical Technol, 2008, DOI:10.1155/2008/359760
[27]Ahmad I S, Reid J F. Evaluation of colour representations for maize images. J Agric Engng Res, 1996, 63: 185–195
[28]Adamsen F J, Pinter P J, Jr, Barnes E M, LaMorte R L, Wall G W, Leavitt S W, Kimball B A. Measuring wheat senescence with a digital camera. Crop Sci., 1999, 39: 719–724
[29]Karcher D E, Richardson M D. Quantifying turfgrass color using digital image analysis. Crop Sci, 2003, 43: 943–951
[30]Wang K-R(王克如), Li S-K(李少昆), Wang C-T(王崇桃), Yang L(杨蕾), Xie R-Z(谢瑞芝), Gao S-J(高世菊), Bai J-H(柏军华). Acquired chlorophyll concentration of cotton leaves with technology of machine vision. Acta Agron Sin (作物学报), 2006, 32(1): 34–40 (in Chinese with English abstract)
[31]Wang F-Y(王方永), Li S-K(李少昆), Wang K-R(王克如), Sui X-Y(隋学艳), Bai J-H(柏军华), Chen B(陈兵), Liu G-Q(刘国庆), Tan H-Z(谭海珍). Obtaining information of cotton population chlorophyll by using machine vision technology. Acta Agron Sin (作物学报), 2007, 33(12): 2041–2046 (in Chinese with English abstract)
[32]Tan H-Z(谭海珍), Li S-K(李少昆), Wang K-R(王克如), Xie R-Z(谢瑞芝), Gao S-J(高世菊), Ming B(明博), Yu Q(于青), Lai J-C(赖军臣), Liu G-Q(刘国庆), Tang Q-X(汤秋香). Monitoring canopy chlorophyll density in seedlings of winter wheat using imaging spectrometer. Acta Agron Sin (作物学报), 2008, 34(10): 1812–1817 (in Chinese with English abstract)
[33]Lichtenthale H K. Chlorophyll and Carotenoids, the Pigments of Photosynthetic Biomembranes. Methods in Enzymology. San Diego, CA: Academic Press, 1987. 148: 350–382
[34]Massart D L, Vandeginste B G M, Deming S M, Michotte Y, Kaufman L. Chenometrics: A Textbook. Elsevier, Amsterdam, 1988
[35]Broge N H, Mortensen J V. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sens Environ, 2002, 81: 45–57
[36]Gamon J A, Surfus J S. Assessing leaf pigment content and activity with a reflectometer. New Phytol, 1999, 143: 105−117
[37]Yao X(姚霞), Wu H-B(吴华兵), Zhu Y(朱艳), Tian Y-C(田永超), Zhou Z-G(周治国), Cao W-X(曹卫星). Relationship between pigment concentration and hyper-spectral parameters in functional leaves of cotton. Cotton Sci (棉花学报), 2007, 19(4): 267–272 (in Chinese with English abstract)
[38]Blackburn G A. Hyperspectral remote sensing of plant pigments. J Exp Bot, 2007, 58: 855−867
[39]Blackburn G A. Remote sensing of forest pigments using airborne imaging spectrometer and LIDAR imagery. Remote Sens Environ, 2002, 82: 311–321
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