Welcome to Acta Agronomica Sinica，

Acta Agron Sin ›› 2010, Vol. 36 ›› Issue (11): 1981-1989.doi: 10.3724/SP.J.1006.2010.01981

• RESEARCH ACTIVITIES • Previous Articles     Next Articles

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 Online:2010-11-12 Published:2010-08-30
  • Contact: LI Shao-Kun,E-mail:Lishk@mail.caas.net.cn;Tel:010-82108891

PDF

1952

Cited

11

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
[1] QIN Lu, HAN Pei-Pei, CHANG Hai-Bin, GU Chi-Ming, HUANG Wei, LI Yin-Shui, LIAO Xiang-Sheng, XIE Li-Hua, LIAO Xing. Screening of rapeseed germplasms with low nitrogen tolerance and the evaluation of its potential application as green manure [J]. Acta Agronomica Sinica, 2022, 48(6): 1488-1501.
[2] GUO Xing-Yu, LIU Peng-Zhao, WANG Rui, WANG Xiao-Li, LI Jun. Response of winter wheat yield, nitrogen use efficiency and soil nitrogen balance to rainfall types and nitrogen application rate in dryland [J]. Acta Agronomica Sinica, 2022, 48(5): 1262-1272.
[3] PENG Xi-Hong, CHEN Ping, DU Qing, YANG Xue-Li, REN Jun-Bo, ZHENG Ben-Chuan, LUO Kai, XIE Chen, LEI Lu, YONG Tai-Wen, YANG Wen-Yu. Effects of reduced nitrogen application on soil aeration and root nodule growth of relay strip intercropping soybean [J]. Acta Agronomica Sinica, 2022, 48(5): 1199-1209.
[4] YAN Yu-Ting, SONG Qiu-Lai, YAN Chao, LIU Shuang, ZHANG Yu-Hui, TIAN Jing-Fen, DENG Yu-Xuan, MA Chun-Mei. Nitrogen accumulation and nitrogen substitution effect of maize under straw returning with continuous cropping [J]. Acta Agronomica Sinica, 2022, 48(4): 962-974.
[5] LI Xin-Ge, GAO Yang, LIU Xiao-Jun, TIAN Yong-Chao, ZHU Yan, CAO Wei-Xing, CAO Qiang. Effects of sowing dates, sowing rates, and nitrogen rates on growth and spectral indices in winter wheat [J]. Acta Agronomica Sinica, 2022, 48(4): 975-987.
[6] YUAN Jia-Qi, LIU Yan-Yang, XU Ke, LI Guo-Hui, CHEN Tian-Ye, ZHOU Hu-Yi, GUO Bao-Wei, HUO Zhong-Yang, DAI Qi-Gen, ZHANG Hong-Cheng. Nitrogen and density treatment to improve resource utilization and yield in late sowing japonica rice [J]. Acta Agronomica Sinica, 2022, 48(3): 667-681.
[7] DING Hong, XU Yang, ZHANG Guan-Chu, QIN Fei-Fei, DAI Liang-Xiang, ZHANG Zhi-Meng. Effects of drought at different growth stages and nitrogen application on nitrogen absorption and utilization in peanut [J]. Acta Agronomica Sinica, 2022, 48(3): 695-703.
[8] FENG Jian-Chao, XU Bei-Ming, JIANG Xue-Li, HU Hai-Zhou, MA Ying, WANG Chen-Yang, WANG Yong-Hua, MA Dong-Yun. Distribution of phenolic compounds and antioxidant activities in layered grinding wheat flour and the regulation effect of nitrogen fertilizer application [J]. Acta Agronomica Sinica, 2022, 48(3): 704-715.
[9] LIU Yun-Jing, ZHENG Fei-Na, ZHANG Xiu, CHU Jin-Peng, YU Hai-Tao, DAI Xing-Long, HE Ming-Rong. Effects of wide range sowing on grain yield, quality, and nitrogen use of strong gluten wheat [J]. Acta Agronomica Sinica, 2022, 48(3): 716-725.
[10] WANG Yan, CHEN Zhi-Xiong, JIANG Da-Gang, ZHANG Can-Kui, ZHA Man-Rong. Effects of enhancing leaf nitrogen output on tiller growth and carbon metabolism in rice [J]. Acta Agronomica Sinica, 2022, 48(3): 739-746.
[11] DONG Yan-Kun, HUANG Ding-Quan, GAO Zhen, CHEN Xu. Identification, expression profile of soybean PIN-Like (PILS) gene family and its function in symbiotic nitrogen fixation in root nodules [J]. Acta Agronomica Sinica, 2022, 48(2): 353-366.
[12] ZHANG Te, WANG Mi-Feng, ZHAO Qiang. Effects of DPC and nitrogen fertilizer through drip irrigation on growth and yield in cotton [J]. Acta Agronomica Sinica, 2022, 48(2): 396-409.
[13] ZHANG Jun, ZHOU Dong-Dong, XU Ke, LI Bi-Zhong, LIU Zhong-Hong, ZHOU Nian-Bing, FANG Shu-Liang, ZHANG Yong-Jin, TANG Jie, AN Li-Zheng. Nitrogen fertilizer reduction and precise application model on mechanical transplanting japonica rice with good taste quality under straw returning in Huaibei Area [J]. Acta Agronomica Sinica, 2022, 48(2): 410-422.
[14] XIE Cheng-Hui, MA Hai-Zhao, XU Hong-Wei, XU Xi-Yang, RUAN Guo-Bing, GUO Zheng-Yan, NING Yong-Pei, FENG Yong-Zhong, YANG Gai-He, REN Guang-Xin. Effects of nitrogen rate on growth, grain yield, and nitrogen utilization of multiple cropping proso millet after spring-wheat in Irrigation Area of Ningxia [J]. Acta Agronomica Sinica, 2022, 48(2): 463-477.
[15] ZHANG Jia-Kang, LI Fei, SHI Shu-De, YANG Hai-Bo. Construction and application of the critical nitrogen concentration dilution model of sugar beet in Inner Mongolia, China [J]. Acta Agronomica Sinica, 2022, 48(2): 488-496.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Add: No. 80 Xueyuan South Rd, Beijing 100081, P. R. China E-mail: zwxb301@caas.cn
Supported by Beijing Magtech Co.Ltd