To promote the use of imaging spectroscopy to assess the nutritional status of apple trees, the models to estimate the chlorophyll content of apple leaves were explored. Spectral data for apple leaves were collected with an imaging spectrometer and then preprocessed with the nine-point moving weighted average method. Correlation analyses were conducted between chlorophyll content and mathematically transformed spectral data. Wavelengths sensitive to chlorophyll content were selected on the basis of the highest correlation coefficients, and partial least squares (PLS), support vector machine (SVM), and random forest (RF) models to estimate chlorophyll content were established and tested. The wavelengths sensitive to chlorophyll content were 414, 424, 429, 439, and 577 nm. The best model was the SVM model with wavelength data subjected to a second order differential of the logarithm transformation log R414)”, (log R424)”, (log R429)”, (log R439)”, (log R577)” as the independent variables. For this model, the coefficient of determination V-R2 was 0.7372, the root mean square error V-RMSE was 0.4477, and the residual predictive deviation V-RPD was 1.8810. Among all the models, this SVM model had the highest V-R2 and V-RPD values and the lowest V-RMSE value.
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References
X. G. Xu, C. J. Zhao, J. H. Wang, W. J. Huang, C. J. Li, and H. J. Liu, Spectrosc. Spect. Anal., 31, 188–191 (2011).
D. N. H. Horler, J. Barber, J. P. Darch, D. C. Ferns, and A. R. Barringer, Adv. Space Res., 3, 175–179 (1983).
A. Maccioni, G. Agati, and P. Mazzinghi, J. Photochem. Photobiol. B, 61, 52–61 (2001).
V. Ciganda, A. Gitelson, and J. Schepers, J. Plant Physiol., 166, 157–167 (2009).
M. Schlemmer, A. Gitelson, J. Schepers, R. Ferguson, Y. Peng, J. Shanahan, and D. Rundquist, Int. J. Appl. Earth Observ., 25, 47–54 (2013).
E. R. Hunt Jr , P. C. Doraiswamy, J. E. McMurtrey, C. S. T. Daughtry, E. M. Perry, and B. Akhmedov, Int. J. Appl. Earth Observ., 21, 103–112 (2013).
Z. N. Gong, Y. L. Zhao, W. J. Zhao, C. Lin, and T. X. Cui, Acta Ecologica Sinica, 34, 5736–5745 (2014).
X. C. Zhu, G. X. Zhao, R. Y. Wang, F. Dong, L. Wang, and T. Lei, Sci. Agricult. Sinica, 43, 1189–1197 (2010).
X. J. Yue, D. P. Quan, T. S. Hong, J. Wang, X. M. Qu, and H. M. Gan, Trans. CSAE, 31, 294–302 (2015).
S. W. Wang, S. Zhao, C. L. Zhang, Z. B. Su, L. F. Wang, and Y. Zhao, Trans. CSAE, 32, 149–154 (2016).
H. S. Li, Plant Physiology and Biochemistry Experimental Principles and Techniques, Beijing, Higher Education Press, 45 (2000).
T. He, Study on Land Quality Hyperspectral Remote Sensing Monitoring Method, Wuhan University, Wuhan, 44–54 (2003).
Y. He, F. Liu, X. L. Li, and Y. N. Shao, Spectroscopy and Imaging Technology in Agriculture, Science Press, Beijing, 93, 109, 145 (2016).
N. Cristianini and J. S. Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Method, Publishing House of Electronics Industry, Beijing, 24–26 (2004).
H. Yan, X. G. Zhang, Y. Q. Ma, and Y. D. Li, Acta Automat. Sinica, 28, 450–455 (2002).
L. Breiman, Machine Learning, 45, 5–32 (2001).
E. Gaston, J. M. Frias, P. J. Cullen, C. P. O'Donnell, and A. A. Gowen, J. Agr. Food Chem., 58, 6226–6233 (2010).
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Published in Zhurnal Prikladnoi Spektroskopii, Vol. 86, No. 3, pp. 425–432, May–June, 2019.
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Yu, R., Zhu, X., Cao, S. et al. Estimation of Chlorophyll Content in Apple Leaves Based on Imaging Spectroscopy. J Appl Spectrosc 86, 457–464 (2019). https://doi.org/10.1007/s10812-019-00841-1
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DOI: https://doi.org/10.1007/s10812-019-00841-1