| Cite this article: | 丁凡,陈震,李长春,等.不同施氮处理下无人机光谱感知冬小麦产量[J].灌溉排水学报,0,():-. |
| DING Fan,CHEN Zhen,LI Changchun,et al.不同施氮处理下无人机光谱感知冬小麦产量[J].灌溉排水学报,0,():-. |
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| UAV Spectral-sensing winter wheat yield under different nitrogen treatments |
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DING Fan1, CHEN Zhen2, LI Changchun1, CHENG Qian2, FEI Shuaipeng2, LI Jingbo1, XU Honggang2, LI Zongpeng3
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1.Henan Polytechnic University;2.Farmland Irrigation Research Institute,Chinese Academy of Agricultural Sciences;3.Henan Agricultural University
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| Abstract: |
| Rapid and accurate prediction of winter wheat yield and construction of the best yield prediction model are of great application value to precision agriculture farmland management. In this study, the canopy spectral information of winter wheat at flowering stage, early filling stage and middle filling stage was collected by the multi-spectral sensor carried by UAV and the vegetation index was extracted. Combining stepwise regression and random forest, the optimal feature was selected and the yield prediction model was established. In the early filling stage, the regression model based on all vegetation features had the highest accuracy in predicting winter wheat yield, with R2 reaching 0.76. The regression model R2 of Green (G), Blue (B), Excess Green (ExG), Excess Red (ExR), Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI) and Normalized Difference Vegetation Index(TNDVI) feature combination was high and AIC (Akaike Information Criterion) was low, reaching 0.73. In the flowering stage, ExG, Near Infrared (NIR), Normalized Difference Index (NDI), Blue, (B) and Modified soil-adjusted Vegetation Index 2 (MSAVI2) had the best yield prediction effect with a low AIC (R2 = 0.71). In the middle filling stage, ExG and NIR had the best yield prediction effect and low AIC (R2 = 0.71). It was feasible to screen out the feature combination with the least collinearity between features by stepwise regression and build a regression model using random forest, which could accurately predict winter wheat yield and provide reference for yield estimation in breeding work. |
| Key words: Multispectral; Vegetation index; Stepwise regression; Random forest; Yield prediction |
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