Abstract
Reflectance spectra of living wheat’s leaves were acquired by portable radiation spectrometer. Multivariate scatter correction (MSC) method was used to preprocess the spectra, and quantitative analysis detection models were built up by artificial neural networks (ANN). The models of chlorophyll, chlorophyll a, and chlorophyll b in leaves were built up and tested. In calibration set and predicted set, for chlorophyll in leaves, the correlation coefficient (R) were 0.927 and 0.818, the standard deviation (SD) were 0.252 and 0.470; for chlorophyll a in leaves, the correlation coefficient (R) were 0.780 and 0.627, the standard deviation (SD) were 0.441 and 0.592; for chlorophyll b in leaves, the correlation coefficient (R) were 0.876 and 0.871, the standard deviation (SD) were 0.205 and 0.259. The results show that, using portable radiation spectrometer to acquire the reflectance spectra, multivariate scatter correction to preprocess the spectra, and artificial neural networks to build up the models, the chlorophyll, chlorophyll a and chlorophyll b in winter wheat’s leaves may be quantitative detected.
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References
Fan, Z., Simone, G., Yuping, B., et al.: Estimation of wheat water status by reflectance measurements. Trans. CSAE 21(S), 215–217 (2005)
Ji, H., Wang, P., Yan, T.: Estimations of chlorophyll and water contents in live leaf of winter wheat with reflectance spectroscopy. Spectrosc. Spectr. Anal. 27(3), 514–516 (2007)
Li, F., Zhao, C., Wang, J., et al.: Diagnosis of nitrogen nutrition of flue-cured tobacco with chlorophyll meter. Plant Nutr. Fertil. Sci. 13(1), 136–142 (2007)
Wang, D., Ji, J., Gao, H.: The effect of MSC spectral pretreatment regions on near infrared spectroscopy calibration results. Spectrosc. Spectr. Anal. 34(9), 2387–2390 (2014)
Yu, D., Cheng, W., Wang, Q., et al.: Construction of analysis model of rice seed components based on near infrared reflectance spectroscopy. J. Light. Scatt. 27(4), 384–389 (2015)
Li, L., Huang, H., Zhao, S., et al.: NIR spectra detection model of protein, fat, total sugar and moisture in rice. J. Chin. Cereals Oils Assoc. 32(7), 121–126 (2017)
Cao, H., Li, D., Liu, L., et al.: Near infrared spectroscopy quantitative analysis model based on incremental neural network with partial least squares. Spectrosc. Spectr. Anal. 34(10), 2799–2803 (2014)
Lai, L., Ma, W., Chen, H.: Wheat protein nondestructive analysis with near infrared reflectance spectroscopy combined with artificial neutral networks. J. China Univ. Metrol. 26(1), 55–59 (2015)
Kuang, J., Guan, X., Liu, J.: Rapid determination of protein and fat contents in raw milk by near infrared spectroscopy analysis. J. Anal. Sci. 31(6), 783–786 (2015)
Zheng, W., Ming, J., Yang, M., et al.: Hyperspectral estimation of rice pigment content based on band depth analysis and BP neural network. Chin. J. Eco-Agric. 25(8), 1224–1235 (2017)
Yao, X., Wang, X., Huang, Y., et al.: Estimation of sugar to nitrogen ratio in wheat leaves with near infrared spectrometry. Chin. J. Appl. Ecol. 26(8), 2371–2378 (2015)
Acknowledgements
The authors acknowledge the National Key Research and Development Program (Grant No. 2016YFD0200602).
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Rao, Z., Zhang, L., Liang, X. (2020). Detection Models Study of Chlorophyll in Winter Wheat’s Leaves by Reflectance Spectra and Artificial Neural Networks. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_48
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DOI: https://doi.org/10.1007/978-3-030-32456-8_48
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