Research On Monitoring Nitrogen Nutrition In Wheat Based On Image And Spectral Information Of Remote Sensing At Near-Ground Scale

As an important crop in China,wheat occupies an important position in agricultural production and strategic food reserves.Nitrogen is an important nutrient element during the growth period of wheat,and also an important basis for determining wheat quality,yield and productivity.Quantitative monitoring of nitrogen concentration has become an important research direction in the field of agricultural remote sensing,and it is the key to the implementation of crop growth monitoring,precision farming management and precision fertilization in the development of smart agriculture.In recent years,crop nitrogen nutrition monitoring based on different remote sensing platforms has been successfully applied,and feature extraction has gradually become a key technology in non-destructive monitoring and diagnosis of crop nitrogen nutrition,which greatly expands the feature expression ability of crop canopy.However,traditional feature extraction usually considers remote sensing data as homogeneous data,and the differences in the types,structures and dimensions of different remote sensing data are often ignored,which not only affects the universality and robustness of the estimation model,but also makes it impossible to accurately obtain the comprehensive features of image and spectrum of different remote sensing data.Therefore,it is necessary to clarify the change regular of the features of image and spectrum of different remote sensing data and the influence on the nitrogen estimation model.It is of great significance to optimize crop cultivation,reduce fertilization and increase efficiency,and promote sustainable agricultural development.Therefore,multi-year wheat field trials with interaction of multiple cultivation factors were carried out in this study.Canopy reflectance spectra,hyperspectral images,RGB images of unmanned aerial vehicle(UAV),and nitrogen concentration of leaf layer in wheat were obtained by using ground feature spectrometers,imaging spectrometers and UAV carrying consumer-grade digital cameras.For the features of different remote sensing monitoring data,the traditional features of image and spectrum of wheat canopy at different growth periods were systematically analyzed,and the sensitive response parameters of different features of image and spectrum to wheat nitrogen nutrition were explored,and the features of image and spectrum and nitrogen concentration of leaf layer in wheat based on different remote sensing data were clarified.Furthermore,the models for estimating the nitrogen concentration of leaf layer in wheat based on the fusion of features of image and spectrum of different remote sensing platforms were constructed,which provided theoretical basis and technical support for monitoring of wheat growth status.First of all,the experimental data of different nitrogen fertilizer levels,different planting densities,and different varieties of wheat fields were used,and the changes regular of traditional features of image and spectrum extracted from different remote sensing monitoring data in different growth periods of wheat were systematically analyzed.The response perfornance of the features after eliminating data redundancy and autocorrelation was ascertained,and its quantitative relationship with nitrogen concentration of leaf layer in wheat at different growth stages.1)For hyperspectral data,the vegetation index constructed based on reflectance and the nitrogen concentration of leaf layer in wheat showed a strong correlation at different growth periods,and the vegetation index showed collinearity.Then the vegetation index was selected based on the random forest algorithm.The results showed that DCNI#,NDRE,NDVI Ⅱ,RVI Ⅳ,VOG1,VOG2,VOG3,MSR have high relative importance.At the same time,the reflection spectrum is processed by the spectral system removal method to extract depth,area,normalized depth parameters of the spectral reflection position(500-673nm,745-980nm,980-1200nm,1200-1359nm,1453-1799nm and 20002400nm)and the spectral absorption position(555-745nm,883-1078nm,1078-1274nm).The random forest algorithm is used to optimize the spectral position and shape features with relatively high importance,including:A_Depthl,A_Areal,A_ND1,A_Depth2,R_Depth1,R_Areal,R_ND1,R_Depth3,Dr,SDr,Rg,Ro.Among them,A_ND1,R_ND1 Rg,Ro and nitrogen concentration of leaf layer in wheat showed negative correlation,and other features showed positive correlation.2)For the hyperspectral image data,the vegetation index and nitrogen concentration of leaf layer in wheat at different growth stages show a strong correlation.Among them,NDVIg-b#,VOG2,VOG3,NPCI,SIPI,PSRI were negatively correlated with nitrogen concentration of leaf layer in wheat,and other vegetation indices were positively correlated.Feature selection was performed based on random forest algorithm,and the results show that NDVIg-b#,SIPI,NPCI,VOG3,VOG2,RVI Ⅰ,SAVI Ⅱ,MTVI2 have high relative importance.And the two absorption positions(557-754nm,900-1030nm)and two reflection positions(500-675nm,754-960nm)of the wheat canopy hyperspectrum were obtained using the spectral system removal method,and then the depth,area,and normalization were extracted.By analyzing the distribution of eigenvalues,it is found that there were obvious differences in different growth periods of wheat,and then the features of the whole growth period were used for correlation analysis,so that the features that have a higher correlation with the nitrogen concentration of the wheat leaf layer were extracted,including Rg,R_Depth1,R_Aear1,R_ND1,A_Depth1,A_Aear1,A_ND 1.3)For the RGB image of the wheat canopy,the correlation analysis between the visible light vegetation index constructed based on the R,G and B channels and the nitrogen concentration of leaf layer in wheat.In addition,six high-frequency sub-images in vertical,horizontal and diagonal directions were obtained through two-layer discrete wavelet decomposition,and energy(E),entropy(En),mean(Mean)and standard deviation(S)were extracted from the high-frequency sub-images.After statistical analysis,it was found that there was serious collinearity among wavelet texture features,and the correlation with the nitrogen concentration of wheat leaf layer was weak.Therefore,eight principal component wavelet texture features were obtained using principal component analysis.Then,a convolutional neural network model under the deep learning framework was used to extract deep features.Deep learning was introduced into the monitoring of wheat nitrogen nutrition,and a convolutional neural network model based on the PyTorch framework was constructed which includes five convolutional layers,three pooling layers and two fully connected layers.Through migration learning,fine-tuning of parameters,and adjustment of the input layer data format to adapt to different remote sensing data analysis.The abstract and complex deep features of the image were extracted based on the end-toend automatic extraction of wheat canopy reflectance spectra,hyperspectral images and UAV RGB images from different remote sensing monitoring platforms,which enhanced the ability of feature semantic expression.1)For hyperspectral data,after removing the noise spectrum,the 1811-dimensional spectral data was retained,and the 64-dimensional sensitive wavelength was extracted based on the successive projections algorithm(SPA),and the 64×64 spectral matrix was used as the input of the convolutional neural network,the 256-dimensional deep features were extracted through convolutional layer,pooling layer and fully connected layer;2)For the hyperspectral image and the RGB image of the wheat canopy,the image size was adjusted to 227 pixel×227 pixel×3 as the input of the convolutional neural network and extracted separately 256-dimensional deep features;the deep features of different convolutional layers on wheat canopy hyperspectral images and UAV RGB images were displayed with feature visualization.The results show that the different deep features automatically extracted have obvious robustness,which reflects the advantages of the convolutional neural network in feature extraction local field and weight sharing.The research results provide technical support for improving the robustness of features.At the same time,the features of image and spectrum of different dimensions were integrated.According to the features of remote sensing data acquired by different remote sensing platforms,the traditional features of image and spectrum based on successive projections algorithm and wavelet transform were extracted from reflectance spectra(vegetation index,spectral position and shape features),hyperspectral images(vegetation index,spectral position and shape features),and UAV RGB images(visible light vegetation index,wavelet texture features).At the same time,the low-level attributes were converted into more robust and abstract features through the convolutional neural network model,and the deep features of the hyperspectral(spectral dimension),the deep features of the hyperspectral image and the UAV RGB image(spatial dimension)were extracted respectively.The correlation between traditional features of image and spectrum and deep features and nitrogen concentration of leaf layer in wheat was analyzed,which help to clarify the feature response regular after eliminating data redundancy and feature autocorrelation.The results of the study improved the estimation accuracy of the nitrogen concentration of leaf layer in wheat.In addition,an estimation model of nitrogen concentration of leaf layer in wheat based on different remote sensing monitoring platforms was constructed.The support vector regression algorithm based on particle swarm optimization optimized two important parameters such as the penalty coefficient C and kernel function parameters g of the S VR model,and solved the problem that the parameters such as penalty factor,kernel function,and sensitivity coefficient were difficult to choose.Through the establishment of PLSR,SVR and PSO-SVR models,and verification based on independent year data,the performance of all models was compared with the root mean square error(RMSE),coefficient of determination(R~2)and residual predictive deviation(RPD)of the prediction model were analyzed in detail.Therefore,the PSOSVR model estimated nitrogen concentration of leaf layer in wheat based on fusion features(traditional features of image and spectrum and deep features)was determined to be the most effective.1)For the wheat canopy hyperspectrum(reflectance spectrum),the PSO-SVR model based on the fusion features has the highest accuracy,and the calibration set R~2 reaches 0.923,which was 8.06%and 2.8%higher than the PLS and SVR models respectively.The validation set R~2 reaches 0.855,which was 9.25%and 3.31%higher than the PLS and SVR models respectively.2)For wheat canopy hyperspectral images,the PSOSVR model based on the fusion features has the highest accuracy,and the calibration set R~2 reaches 0.9251,which was 5.04%and 1.12%higher than that of the PLS and SVR models respectively.The validation set R~2 reaches 0.8663,which was 4.85%and 0.48%higher than tthat of he PLS and SVR models respectively.3)For the wheat canopy UAV RGB image,the PSO-SVR model based on fusion features has the highest accuracy,and the calibration set R~2 reaches 0.9172,which was 9.71%and 2.92%higher than the PLS and SVR models respectively.The validation set R~2 reaches 0.8562,which was 11.19%and 6.16%higher than that of the PLS and SVR models respectively.