Yield Estimation of Winter Wheat Based on Multiple Remotely Sensed Parameters and VMD-GRU

被引：0
|
作者：
Guo F. [1 ,2 ]
Wang P. [1 ,2 ]
Liu J. [3 ]
Li H. [4 ]
机构：
[1] College of Information and Electrical Engineering, China Agricultural University, Beijing
[2] Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing
[3] College of Land Science and Technology, China Agricultural University, Beijing
[4] Shaanxi Provincial Meteorological Bureau, Xi’an
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2024年 / 55卷 / 01期
基金：
中国国家自然科学基金;
关键词：
gated recurrent unit; remotely sensed parameter; variational mode decomposition; winter wheat; yield estimation;
D O I：
10.6041/j.issn.1000-1298.2024.01.015
中图分类号：
学科分类号：
摘要：
In order to fully exploit the time-series information and trend information of time-series remotely sensed parameters and further improve the yield estimation accuracy of winter wheat, vegetation temperature condition index (VTCI), leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR), which were closely related to the growth and development of winter wheat, were selected as remotely sensed parameters, and a neural network was constructed based on variational mode decomposition (VMD) and gated recurrent unit (GRU). The VMD algorithm was applied to decompose each remotely sensed parameter series into multiple sets of intrinsic mode function (IMF) components, and the IMF components that were highly correlated with the original remotely sensed parameter series were selected for feature reconstruction, and the reconstructed features were used as the input of the GRU network to develop a combined model for yield estimation of winter wheat. The results showed that the VMD 一 GRU model for yield estimation had a coefficient of determination of 0.63, root mean squared error of 448.80 kg/hm2, and mean relative error of 8.14%, with a highly significant correlation level (P < 0.01), and its accuracy was better than that of the single model for yield estimation, indicating that the combined model for yield estimation can extract multi-scale and multi-level features of non-stationary time series and fully explore the internal linkage between remotely sensed parameters in each growth stage of winter wheat to obtain accurate yield estimation results and improve interpretability of model for yield estimation. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.
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页码：164 / 174and185
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