关键词: 无人机, 荞麦, 叶绿素, 植被指数, 相关性

Abstract: In order to verify the possibility of estimating the chlorophyll content of buckwheat canopy by using low‑altitude unmanned aerial vehicle（UAV）with multi‑spectral camera，and to explore the effect of selenium（Se）on the chlorophyll and spectral characteristics of buckwheat canopy，Jinsage No.6 sweet buckwheat and Jinsage No.9 bitter buckwheat were selected as the research objects，and a experimental study was conducted in Taigu District，Jinzhong City，Shanxi Province.Buckwheat multispectral images were collected under different selenium levels at different stages by using UAV with multi‑spectral camera，and the relative chlorophyll content（SPAD value）was synchronously measured in the field.Firstly，the SPAD values of buckwheat canopy under different selenium application levels were analyzed.Secondly，the reflectance of buckwheat canopy under five bands was obtained by extracting the spectral information of multi‑spectral images.On this basis，the spectral characteristics of buckwheat at full bloom and filling stage were analysed，11 vegetation indices were constructed using the reflectance in five bands，and the absolute magnitude of the correlation coefficients between the 16 spectral variables and the measured SPAD values were obtained by Pearson correlation analysis.Partial least square regression（PLSR），principal component regression（PCR），support vector machine regression（SVR）and back propagation neural network（BPNN）were used to construct a buckwheat canopy SPAD monitoring model，and the optimal estimation model was determined by accuracy test.The results showed that moderate application of selenium fertilizer could increase SPAD value of buckwheat，while excessive application could inhibit SPAD value.The blue，red，red‑edge and NIR bands showed strong correlation among the five bands，and the NIR band showed high and stable correlation. In terms of vegetation index，the correlation coefficients（|r|）of standardized precipitation index（SPI），green chlorophyll index（GCI），green normalized difference vegetation index（GNDVI），normalized green light index（NGI），transformed optimized soil‑regulated vegetation index（TOSAVI），transformed chlorophyll absorption ratio index（TCARI），and triangular vegetation index（TVI）ranged from 0. 50 to 0. 91，which indicated a good correlation.At the full‑bloom stage，BPNN had the best prediction effect，the correlation coefficient of prediction set（R²_P）was 0.97，and the root mean square error（RMSE）of prediction set was 0.95.In the filling stage，the prediction effect of SVR was better than other models，R²_Pwas 0.96，and RMSE was 0.45.At the full‑bloom stage and filling stage，PLSR showed the best performance，R²_P was 0.98，RMSE was 0.28.By comparing all the models，SVR showed higher stability and accuracy（R²_P and RMSE ranges were 0.94—0.96 and 0.45—0.82，respectively，and RPD values were greater than 3.00）.These results show that UAV with a multispectral camera can achieve rapid monitoring of buckwheat canopy chlorophyll content in the field，providing a reference for optimization of the model algorithm for low altitude prediction of chlorophyll content by UAV.

Key words: UAV, Buckwheat, Chlorophyll, Vegetation index, Correlation