Spectroscopic determination of chlorophyll content in sugarcane leaves for drought stress detection
Author:
Funder
Young Scientists Fund
Publisher
Springer Science and Business Media LLC
Subject
General Agricultural and Biological Sciences
Link
https://link.springer.com/content/pdf/10.1007/s11119-023-10082-0.pdf
Reference57 articles.
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2. Amarasingam, N., Salgadoe, A. S. A., Powell, K., Gonzalez, L. F., & Natarajan, S. (2022). A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops. Remote Sensing Applications: Society and Environment, 26, 100712. https://doi.org/10.1016/j.rsase.2022.100712
3. Araújo, M. C. U., Saldanha, T. C. B., Galvão, R. K. H., Yoneyama, T., Chame, H. C., & Visani, V. (2001). The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/S0169-7439(01)00119-8
4. Bhadra, S., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Newcomb, M., Shakoor, N., & Mockler, T. C. (2020). Quantifying leaf chlorophyll concentration of sorghum from hyperspectral data using derivative calculus and machine learning. Remote Sensing. https://doi.org/10.3390/rs12132082
5. Bylander, T. (2002). Estimating generalization error on two-class datasets using out-of-bag estimates. Machine Learning, 48, 1–3. https://doi.org/10.1023/A:1013964023376.
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