Hyperspectral Estimation of Nitrogen Content in Winter Wheat Leaves Based on Unmanned Aerial Vehicles

Abstract : Leaf nitrogen content is an important index of crop growth and plays an important role in crop growth and development. In this paper, the hyperspectral data of winter wheat and the leaf nitrogen content is used to study winter wheat on flagging stage, flowering stage and grain filling stage. The estimation model of nitrogen content in winter wheat leaves at different growth stages is constructed by using partial least squares method and verified by using a cross-validation method. The results showed that R2 and the RMSE of the three growth stages were 0.53, 0.68, 0.64 and 0.331%, 0.246% and 0.406% respectively, and R2 and RMSE of model validation were 0.44, 0.71, 0.64 and 0.369%, 0.235% and 0.410%. Both the prediction model and the verification model had high reliability. Therefore, it is feasible for UAV to carry hyperspectral monitoring system for retrieving nitrogen content of winter wheat leaves.
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Liu Mingxing, Li Changchun, Feng Haikuan, Pei Haojie, Li Zhenhai, et al.. Hyperspectral Estimation of Nitrogen Content in Winter Wheat Leaves Based on Unmanned Aerial Vehicles. 11th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Aug 2017, Jilin, China. pp.321-339, ⟨10.1007/978-3-030-06179-1_33⟩. ⟨hal-02111529⟩

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