Estimation of chlorophyll content in maize canopy using wavelet denoising and SVR method
Keywords:
maize canopy, spectral reflectance, wavelet denoising, SVR model, chlorophyll contentAbstract
In order to estimate the chlorophyll content of maize plant non-destructively and rapidly, the research was conducted on maize at the heading stage using spectroscopy technology. The spectral reflectance of maize canopy was measured and processed following wavelet denoising and multivariate scatter correction (MSC) to reduce the noise influence. Firstly, the signal to noise ratio (SNR) and curve smoothness (CS) were used to evaluate the denoising effect of different wavelet functions and decomposition levels. As a result, the Sym6 wavelet basis function and the 5th level decomposition were determined to denoise the original signal. The MSC method was used to eliminate the scattering effect after denoising. Then three spectral ranges were extracted by interval partial least squares (IPLS) including the 525-549 nm, 675-749 nm and 850-874 nm. Finally, the chlorophyll content estimation model was developed by using support vector regression (SVR) method. The calibration Rc2 of the SVR model was 0.831, the RMSEC was 1.3852 mg/L; the validation Rv2 was 0.809, the RMSEP was 0.8664 mg/L. The results show that the SNR and CS indicators can be used to select the parameters for wavelet denoising and model can be used to estimate the chlorophyll content of maize canopy in the field. Keywords: maize canopy, spectral reflectance, wavelet denoising, SVR model, chlorophyll content DOI: 10.25165/j.ijabe.20181106.3072 Citation: Liu H J, Li M Z, Zhang J Y, Gao D H, Sun H, Yang L W. Estimation of chlorophyll content in maize canopy using wavelet denoising and SVR method. Int J Agric & Biol Eng, 2018; 11(6): 132–137.References
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[2] Ramoelo A, Dzikiti S, van Deventer H, Maherry A, Cho M A, Gush M. Potential to monitor plant stress using remote sensing tools. Journal of Arid Environments, 2015; 113(2): 134–144.
[3] Kalacska M, Lalonde M, Moore T R. Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: Scaling from leaf to image. Remote Sensing of Environment, 2015; 169: 270–279.
[4] Huang W J, Lu J J, Ye H C, Kong W P, Mortimer A H, Shi Y. Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis. International Journal of Agricultural and Biological Engineering, 2018; 11(2): 145–152.
[5] Ciganda V, Gitelson A, Schepers J. Non-destructive determination of maize leaf and canopy chlorophyll content. Journal of Plant Physiology, 2009; 166(2): 157–167.
[6] Chen P F, Haboudance D, Tremblay N, Wang J H, Vigneault P, Li B G. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sensing of Environment, 2010; 114(9): 1987–1997.
[7] Schlemmer M, Gitelson A, Schepers J, Ferguson R, Peng Y, Shanahan J, et al. Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. International Journal of Applied Earth Observation and Geoinformation, 2013; 25: 47–54.
[8] Rossini M, Cogliati S, Meroni M, Migliavacca M, Galvagno M, Busetto L, et al. Remote sensing-based estimation of gross primary production in a subalpine grassland. Biogeosciences, 2012; 9(7): 2565–2584.
[9] Sun H, Zhao Y, Zhang M, Wen Y, Li M Z, Yang W, et al. Multi-spectral image detection for maize canopy's chlorophyll content in jointing stage. Transactions of the CSAE, 2015; 31(Supp.2): 186–192. (in Chinese)
[10] Deng X L, Li M Z, Zheng L H, Zhang Y, Sun H. Estimating chlorophyll content of apple leaves based on preprocessing of reflectance spectra.
Transactions of the CSAE, 2014; 30(14): 140–147.
[11] Chu X L, Yuan H F, Lu W Z. Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique. Progress in Chemistry, 2004; 16(4): 528–542. (in Chinese)
[12] Ding Y J, Li M Z, Zheng L H, Zhao R J, Li X H, An D K. Prediction of cholorphyll content of greenhouse tomato using wavelet transform combined with NIR spectra. Spectroscopy and Spectral Analysis, 2011; 31(11): 2936–2939. (in Chinese)
[13] Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 1990; 36(5): 961–1005.
[14] Li T H, Shi G Y, Wei M, Wang J M, Hou J L. Wavelet denoising in prediction model of tomato Vitamin C content using NIR spectroscopy. Transactions of the CSAM, 2013; 44(S1): 200–204. (in Chinese)
[15] Liang L, Yang M M, Zang Z. Determination of wheat canopy nitrogen content ratio by hyperspectral technology based on wavelet denoising and support vector regression. Transactions of the CSAE, 2010; 26(12): 248–253. (in Chinese)
[16] Martens H, Stark E. Extended multiplicative signal correction and spectral interference subtraction: New preprocessing methods for near infrared spectroscopy. Journal of Pharmaceutical & Biomedical Analysis, 1991; 9(8): 625–635.
[17] Maleki M R, Mouazen A M, Ramon H, De Baerdemaeker J. Multiplicative scatter correction during on-line measurement with near infrared spectroscopy. Biosystems Engineering, 2007; 96(3): 427–433.
[18] Galvão R K H, Araujo M C U, José G E, Pontes M J C, Silva E C, Saldanha T C B. A method for calibration and validation subset partitioning. Talanta, 2005; 96(4): 736–740.
[19] Zhou X H, Xiang B R, Wang Z M, Zhang M. Determination of quercetin in extracts of ginkgo biloba l. leaves by near‐infrared reflectance spectroscopy based on interval partial least squares (iPLS) model. Analytical Letters, 2007; 40(18): 3383–3391.
[20] Nørgaard L, Saudland A, Wagner J, Nielsen J P, Munck L, Engelsen S B. Interval partial least-squares regression (iPLS): A comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy, 2000; 54(3): 413–419.
[21] Vapnik V N. The nature of statistical learning theory. New York: Springer-Verlag, 1995.
[22] Igne B, Reeves J I, Mccarty G, Hively W, Lund E, Hurburgh C. Evaluation of spectral pretreatments, partial least squares, least squares support vector machines and locally weighted regression for quantitative spectroscopic analysis of soils. J. Near Infrared Spectrosc, 2010; 18(3): 167.
[23] Chang C C, Lin C-J. LIBSVM: A library for support vector machines. 2001.
[24] Cen H Y, He Y, Zhang H, Feng F Q. Rapid measurement of citric acids in orange juice using visible and near infrared reflectance spectroscopy. Spectroscopy and Spectral Analysis, 2007; 27(9):1747–1750.
[25] Wang G Q, Wang W, Fang Q Q, Jiang H, Xin Q C, Xue B L. The application of discrete wavelet transform with improved partial least-squares method for the estimation of soil properties with visible and near-infrared Spectral Data. Remote Sens, 2018; 10(6): 867–884.
[26] Yi T, Li H, Zhao X. Noise smoothing for structural vibration test signals using an improved wavelet thresholding technique. Sensors, 2012; 12(8): 11205–11220.
[27] Li M Z. Spectral analysis technique and its application. Beijing: Science Press, 2006; pp.176–180.
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Published
2018-12-08
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Liu, H., Li, M., Zhang, J., Gao, D., Sun, H., & Yang, L. (2018). Estimation of chlorophyll content in maize canopy using wavelet denoising and SVR method. International Journal of Agricultural and Biological Engineering, 11(6), 132–137. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/3072
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Information Technology, Sensors and Control Systems
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