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Effects of UAV Flight Height on Prediction Model of Plant Nitrogen Accumulation in Winter Wheat
JING Yuhang, GUO Yan, ZHANG Huifang, RONG Yasi, ZHANG Shaohua, FENG Wei, WANG Laigang, HE Jia, LIU Haijiao, ZHENG Guoqing
Journal of Henan Agricultural Sciences
2022, 51 (2):
147-158.
DOI: 10.15933/j.cnki.1004-3268.2022.02.018
UAV has the advantage of obtaining crop information quickly，efficiently and non‑destructively，and is widely used in modern agriculture. However，the flight height of UAV directly affects the efficiency of obtaining crop information. In this study，UAV remote sensing images with different resolutions were obtained at the jointing，flowering and filling stages of winter wheat by setting flight altitudes of 30 m， 60 m and 90 m，to explore and analyze the impact of UAV flight altitude on the prediction model of winter wheat plant nitrogen accumulation. Firstly，six vegetation indices（NDVI，RDVI，RERDVI，GBNDVI，OSAVI，EXG）and four texture features（Green‑mean，Green‑sm，Red‑mean，Red‑var）were screened out by correlation and collinearity analysis between different height of vegetation indices，texture features and nitrogen accumulation of winter wheat. Based on the selected vegetation index and texture characteristics，the prediction models of nitrogen accumulation of winter wheat plant were established with vegetation index，texture feature and vegetation index+texture feature by using PLSR and BPNN methods，and the models were cross‑verified at different heights. The stability of the models was analyzed by R²，RMSE and RPD indices. The results showed that the prediction models established with vegetation index，texture feature and vegetation index+texture feature extracted from 30 m UAV images had the best stability by the two methods. The
R
2
，RMSE and
RPD ranges of the three models with different information were 0.57—0.89，1.27—4.16 g/m2 and 1.67—3.65，respectively. The stability of BPNN model constructed under the three kinds of modeling information was better than that of PLSR on the whole.The
R
2 and
RPD of the verification model were improved in the range of 0.01—0.39 and 0.05—1.44，respectively.
RMSE decreased by 0.08—8.53 g/m
2.The order of stability of the prediction models for plant nitrogen accumulation was vegetation index+texture feature>vegetation index>texture feature. The vegetation index，texture feature and vegetation index+texture feature of remote sensing images from three UAV flight heights were combined to predict plant nitrogen accumulation，which could improve the estimation accuracy（
R
2
，RMSE and
RPD ranges were 0.89—0.93，1.80—2.03 g/m
2 and 3.54—4.03，respectively）.Therefore，when considering the efficiency and accuracy，the flight height of UAV should be increased appropriately for efficient nitrogen accumulation prediction with comprehensive utilization of vegetation index and texture feature.
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