Nitrogen concentration monitoring in winter wheat plants based on CARS characteristic band screening
[Purpose]Nitrogen is an essential nutrient element for crop growth,development,yield,and quality.Efficient,non-destructive and accurate assessment of crop nitrogen status enables monitoring of crop growth,enhances nitrogen fertilizer application levels and efficiency,and reduces agricultural non-point source pollution resulting from excessive fertilizer application.[Method]This study conducted SG smoothing and first-order derivative preprocessing on three years of hyperspectral data(2020-2022).Correlation analysis(CA)and competitive adaptive reweighted sampling(CARS)(CA-CARS)were applied to investigate the relationship between the first-order derivative of the spectrum and plant nitrogen concentration(PNC),identifying the sensitive spectral bands under different nitrogen treatments at the jointing stage.The most sensitive bands were then selected to construct a vegetation index,upon which a univariate linear monitoring model for winter wheat PNC was developed.The data from 2020 and 2022 were used as a training set for modeling,while the data from 2021 served as the validation set for model accuracy assessment.[Result](1)The study showed that under different nitrogen levels in the integrated three-year nodulation period,the highly sensitive band locations of winter wheat PNC were mainly the blue-green band(495 nm-503 nm),the red-edge range(736 nm-750 nm),and the near-infrared range(751 nm-753 nm,751 nm-753 nm,761 nm-765 nm,773 nm-779 nm,922 nm,937 nm-938 nm,1 016 nm-1 032 nm,1 083 nm-1 088 nm,1 127 nm,1 142 nm-1 145 nm,1 292 nm-1 300 nm).(2)CARS identified 6 characteristic bands,specifically 459 nm,682 nm,721 nm,746 nm,1 049 nm,and 1 175 nm.(3)A total of 15 canopy ratio nitrogen indices(CRNI)were constructed using these characteristic bands,with CRNI 10 exhibiting the highest model accuracy and the lowest root mean square error(RMSE).The coefficient of determination(R2)and RMSE for the training and validation sets were R2=0.785,R2=0.679,RMSE=0.254 and RMSE=0.332,respectively.These results indicated that the CRNI model developed in this study was more generalizable in PNC monitoring.[Conclusion]The PNC inversion model constructed by the feature parameters screened by the combination of CA-CARS can effectively improve the accuracy,migration and stability of the PNC monitoring model.
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