Estimation of chlorophyll content in pepper leaves using spectral transmittance red-edge parameters
Keywords:
pepper leaf, chlorophyll content, red-edge parameters, ridge regressionAbstract
The objective of this work was to monitor the growth status of pepper and provide precise guidance on fertilization through non-destructive detection methods for chlorophyll content based on spectral transmittance. The analysis of the narrower red-edge spectral region (680-760 nm) reduced the requirements for light sources and light detection sensors, and provided a simpler and more accurate method of data acquisition for the process of developing instruments for estimating chlorophyll content in leaves. The red-edge region of spectral transmittance was demonstrated to be closely related to chlorophyll content. Regression models for estimating chlorophyll content with seven different methods were developed using the four red-edge parameters extracted from the red-edge region. The problems of multicollinearity of red-edge parameters and errors in model coefficients were solved by the ridge regression method in the process of building a multivariate regression model. The results indicated that the ridge regression method reduces the errors of the model coefficients and constant terms while improving the detection accuracy, thus the ridge regression model could estimate the leaf chlorophyll content more accurately and repeatedly. Keywords: pepper leaf, chlorophyll content, red-edge parameters, ridge regression DOI: 10.25165/j.ijabe.20221505.7350 Citation: Huang S, Wu Y, Wang Q L, Liu J L, Han Q Y, Wang J F. Estimation of chlorophyll content in pepper leaves using spectral transmittance red-edge parameters. Int J Agric & Biol Eng, 2022; 15(5): 85–90.References
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[2] Blackburn G A, Ferwerda J G. Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sensing of Environment, 2008; 112(4): 1614–1632.
[3] Kochubey S M, Kazantsev T A. Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content. Journal of Plant Physiology, 2007; 164(12): 1648–1655.
[4] Peng Y, Gitelson A A. Application of chlorophyll-related vegetation indices for remote estimation of maize productivity. Agricultural and Forest Meteorology, 2011; 151(9): 1267–1276.
[5] Zhou W, Shi R H, Wu N, Zhang C, Tian B. Spectral response and the retrieval of canopy chlorophyll content under interspecific competition in wetlands - Case study of wetlands in the Yangtze River Estuary. Earth Science Informatics, 2021; 14(3): 1467–1486.
[6] Pfündel E E. Simultaneously measuring pulse-amplitude-modulated (PAM) chlorophyll fluorescence of leaves at wavelengths shorter and longer than 700 nm. Photosynthesis Research, 2021; 147: 345–358.
[7] Li H, Wei Z H, Wang X, Xu F. Spectral characteristics of reclaimed vegetation in a rare earth mine and analysis of its correlation with the chlorophyll content. Journal of Applied Spectroscopy, 2020; 87(3): 553–562.
[8] Zhu W J, Li J Y, Li L, Wang A C, Wei X H, Mao H P. Nondestructive diagnostics of soluble sugar, total nitrogen and their ratio of tomato leaves in greenhouse by polarized spectra-hyperspectral data fusion. Int J Agric & Biol Eng, 2020; 13(2): 189–197.
[9] Qi H X, Wu Z Y, Zhang L, Li J W, Zhou J K, Jun Z, et al. Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction. Computers and Electronics in Agriculture, 2021; 187: 106292. doi: 10.1016/j.compag.2021.106292.
[10] Cai Y, Miao Y X, Wu H, Wang D. Hyperspectral estimation models of winter wheat chlorophyll content under elevated CO2. Frontiers in Plant Science, 2021; 12: 642917. doi: 10.3389/fpls.2021.642917.
[11] Wu B, Huang W J, Ye H C, Luo P L, Ren Y, Kong W P. Using multi-angular hyperspectral data to estimate the vertical distribution of leaf chlorophyll content in wheat. Remote Sensing, 2021; 13(8): 1501–1501.
[12] Zhu W X, Sun Z G, Yang T, Li J, Peng J B, Zhu K Y, et al. Estimating leaf chlorophyll content of crops via optimal unmanned aerial vehicle hyperspectral data at multi-scales. Computers and Electronics in Agriculture, 2020; 178: 105786. doi: 0.1016/j.compag.2020.105786.
[13] Chen S M, Ma L H, Hu T T, Luo L. Nitrogen content diagnosis of apple trees canopies using hyperspectral reflectance combined with PLS variable extraction and extreme learning machine. Int J Agric & Biol Eng, 2021; 14(3): 159–166.
[14] Ignat T, Schmilovitch Z, Feföldi J, Bernstein N, Steiner B, Egozi H, et al. Nonlinear methods for estimation of maturity stage, total chlorophyll, and carotenoid content in intact bell peppers. Biosystems Engineering, 2013; 114(4): 414–425.
[15] He D X, Hu J X. Plant nutrition indices using leaf spectral transmittance for nitrogen detection. Trans of the CSAE, 2011; 27(4): 214–218, 397. (in Chinese)
[16] Ding Y J, Li M Z, Zheng L H, Zhao R J, Li X H, An D K. Prediction of chlorophyll content of greenhouse tomato using wavelet transform combined with NIR spectra. Spectroscopy and Spectral Analysis, 2011; 31(11): 2936–2939. (in Chinese)
[17] Luciano S, Richard F, John S, Viacheslav A, Donald R, David M, et al. Water and nitrogen effects on active canopy sensor vegetation indices. Agronomy Journal, 2011; 103: 1815–1826.
[18] Jiang C H, Chen Y W, Wu H H, Li W, Zhou H, Bo Y M, et al. Study of a high spectral resolution hyperspectral LiDAR in vegetation red-edge parameters extraction. Remote Sensing, 2019; 11(17): 2007. doi: 10.3390/rs11172007.
[19] Ren H R, Zhou G S, Zhang X S. Estimation of green aboveground biomass of desert steppe in Inner Mongolia based on red-edge reflectance curve area method. Biosystems Engineering, 2011; 109(4): 385–395.
[20] Ding Y J, Zhang J J, Li X H, Li M Z. Estimation of chlorophyll content of tomato leaf using spectrum red edge position extraction algorithm. Trans of the CSAM, 2016; 47(3): 292–297, 318. (in Chinese)
[21] Li L T, Ren T, Ma Y, Wei Q Q, Wang S Q, Li X K, et al. Evaluating chlorophyll density in winter oilseed rape (Brassica napus L.) using canopy hyperspectral red-edge parameters. Computers and Electronics in Agriculture, 2016; 126: 21–31.
[22] Wen P F, Shi Z J, Li A, Ning F, Zhang Y H, Wang R, et al. Estimation of the vertically integrated leaf nitrogen content in maize using canopy hyperspectral red-edge parameters. Precision Agriculture, 2021; 22(3): 984–1005.
[23] Zheng T, Liu N, Wu L, Li M Z. Estimation of chlorophyll content in potato leaves based on spectral red-edge position. IFAC Papers on Line, 2018; 51(17): 602–606.
[24] Wang J F, He D X, Song J X, Dou H J, Du W F. Non-destructive measurement of chlorophyll in tomato leaves based on spectral transmittance. Inter J Agric & Biol Eng, 2015; 8(5): 73–78.
[25] Raymond H E, Daughtry C. Chlorophyll meter calibrations for chlorophyll content using measured and simulated leaf transmittances. Agronomy Journal, 2014; 106(3): 931–939.
[26] Zhang J H, Han C, Li D P. New vegetation index monitoring rice chlorophyll concentration using leaf transmittance spectra. Sensor Letters, 2010; 8(1): 16–21.
[27] Lichtenthaler H K, Wellburn A R. Determinations of total carotenoids and chlorophyll a and b leaf extracts in different solvents. Biochemical Society Transactions, 1983; 11(5): 591–592.
[28] Zohre E K, Fatemeh R, Mohsen E K, Somayeh N. Investigation of the relationship between dust storm index, climatic parameters, and normalized difference vegetation index using the ridge regression method in arid regions of Central Iran. Arid Land Research and Management, 2020; 34(3): 239–263.
[29] Hoerl A E, Kennard R W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 1970; 12(1): 55–67.
[30] Mcdonald G C. Ridge regression. Wires Computational Statistics, 2009; 1(1): 93–100.
[31] Dawson T P, Curran P J. Technical note a new technique for interpolating the reflectance red-edge position. International Journal of Remote Sensing, 1998; 19(11): 2133–2139.
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Published
2022-11-01
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Huang, S., Wu, Y., Wang, Q., Liu, J., Han, Q., & Wang, J. (2022). Estimation of chlorophyll content in pepper leaves using spectral transmittance red-edge parameters. International Journal of Agricultural and Biological Engineering, 15(5), 85–90. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7350
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Animal, Plant and Facility Systems
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