A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.

Characterization and properties of a Sr2Si5N8:Eu^2+-based light-conversion agricultural film

In this paper,the fracture surfaces of a light-conversion film were observed using a scanning electron microscope(SEM),and then,the fluorescence spectra and mechanical properties of the film were tested.The SEM results show that the average diameter of the light conversion agent is 500 nm.The results of the mechanical properties tests show that the tensile strengths of light-conversion film increase from9.86 to 12.16 MPa and that the strain at breakage increases from 2.37%to 2.75%.In addition,the application effects of the light-co nversion film were studied.The results indicate that plant height,length of the maximum leaf,width of the maximum leaf and breadth of the plants increase by 24.43%,15.30%,15.60%and 19.07%,respectively.The quality of Chinese flowering cabbages treated with the lightconversion film is superior,including a 9.09%increase in the soluble protein content,a 21.27%increase in the polyphenol content,and a 19.15%increase in the soluble sugar content.Based on these results,light-conversion films can be applied in agricultural production.