Coupling continuous wavelet transform with machine learning to improve water status prediction in winter wheat
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
General Agricultural and Biological Sciences
Link
https://link.springer.com/content/pdf/10.1007/s11119-023-10036-6.pdf
Reference67 articles.
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2. Blackburn, G. A., & Ferwerda, J. G. (2008). Retrieval of chlorophyll concentration from leaf reflectance spectra using wavelet analysis. Remote Sensing of Environment, 112(4), 1614–1632. https://doi.org/10.1016/j.rse.2007.08.005
3. Bressan, T. S., Kehl de Souza, M., Girelli, T. J., & Junior, F. C. (2020). Evaluation of machine learning methods for lithology classification using geophysical data. Computers & Geosciences, 139, 104475. https://doi.org/10.1016/j.cageo.2020.104475
4. Carter, G. A. (1991). Primary and secondary effects of water-content on the spectral reflectance of leaves. American Journal of Botany, 78(7), 916–924. https://doi.org/10.2307/2445170
5. Chen, S., Gao, Y., Fan, K., Shi, Y., Luo, D., Shen, J., Ding, Z., & Wang, Y. (2021a). Prediction of drought-induced components and evaluation of drought damage of tea plants based on hyperspectral imaging. Frontiers in Plant Science, 12, 695102. https://doi.org/10.3389/fpls.2021.695102
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