Abstract
In this paper, a backpropagation (BP) neural network algorithm and a multiple factor regression (MR) algorithm are presented to improve the performance of the prediction of wheat growth. By applying the BP neural network algorithm and the MR algorithm, the corresponding Leaf Area Index (LAI) and Soil Plant Analysis Development (SPAD) values can be regressed from the Thematic Mapping (TM) data. The experimental result demonstrates that the designed framework has a better performance, which can effectively predict desired parameters and provide a promising solution for the crop growth monitoring. For finding a better solution for the crop growth monitoring, the performance of the BP neural network and the MR algorithms have been investigated.
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Acknowledgements
The authors wish to thank the support from Newton Network + project: VIP-STB (Scale-up Village to County/Province Level to support Science and Technology at Backyard Programme).
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Fang, Y. et al. (2020). Wheat Growth Assessment for Satellite Remote Sensing Enabled Precision Agriculture. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_275
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DOI: https://doi.org/10.1007/978-981-13-9409-6_275
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