Nitrogen Content Monitoring Of Corn Canopy Based On UAV Multi-Source Data Fusion

Corn is a high-yield food crop in China,which is meaningful to maintaining food security.Especially,nitrogen is an essential element in the process of plant life activities,and it has an indicative and decisive effect on the photosynthesis,growth and development of vegetation.Therefore,how to timely and accurately monitor the nitrogen content of corn in each growth period and formulate the corresponding nitrogen fertilizer application plan is the key to improve the quality of corn.The traditional method of corn nitrogen monitoring is to manually take field samples,and then take the samples back to the laboratory for measurement.This method which consumes a lot of manpower and material resources is inefficient,and can cause damage to field crops.When surveyors conduct manual sampling,in manual sampling,human error is inevitable,and it is difficult to form a uniform standard with this method.Therefore,the use of remote sensing technology in agriculture has advantages.Through with the development of remote sensing technology,the advantages of using remote sensing to monitor agricultural conditions are obvious.With the help of UAV remote sensing platform,fast and efficient data collection can be realized,which is convenient for later data processing and inversion research of various crop indicators.This thesis uses the Xiaotangshan National Precision Agriculture Research Demonstration Base in Changping District,Beijing as the research area to conduct experiments.The UAV remote sensing platform is equipped with digital cameras and multi-spectral sensors to collect UAV remote sensing images of corn tasseling and filling periods in 2017 and 2019.Launched the inversion study of Leaf Nitrogen Concentration(LNC)based on the fusion of Unmanned Aerial Vehicle multi-source image information.The main research content and conclusions of the thesis are as follows:(1)Research on remote sensing monitoring of corn nitrogen based on UAV multi-source image fusion.Through the GS(Gram-schmidt)fusion method,the UAV high definition digital image and multi-spectral image are fused at the pixel level.Construct a typical canopy spectral index for UAV HD digital images,UAV multi-spectral images and fusion images,and analyze the correlation between the canopy spectral index and the measured LNC of corn,and select the top five spectral indices with the best correlation.Random Forest(RF)classification method is used to eliminate background noises such as soil and shadows,and the influence of soil background factors on the accuracy of the corn LNC model is discussed.Finally,two algorithms,Least Absolute Shrinkage and Selection Operator(LASSO)and Partial Least Squares Regression(PLSR)are used to comprehensively evaluate the accuracy of the corn LNC inversion model under different conditions.The results show that the accuracy of the corn LNC inversion model is improved after the digital image and the multi-spectral image are fused.After the multi-spectral image is fused,the PLSR model will increase the R~2 by 0.25,reduce the RMSE by 0.03,and reduce the NRMSE by 2.20% when the soil background is removed during the tasseling and filling period.The corn LNC inversion model has the best inversion effect in the filling period,the R~2 of the LASSO model and the PLSR model were increased by0.15 and 0.10 respectively compared with the tasseling and filling period;and the LASSO model was better than the PLSR model.(2)Research on remote sensing monitoring of corn based on the feature-level fusion of UAV multi-source image and deep learning methods.The canopy spectral indices of the constructed UAV HD digital images and UAV multi-spectral images were screened by gray correlation analysis for the five spectral indices with the highest correlation with corn LNC as corn canopy spectral feature information;through the gray co-occurrence matrix Extract the texture feature information of digital images;pass spectral feature information and texture feature information through Random Forest Regression(RFR),Support Vector Regression(SVR)and Deep Neural Networks(DNN)algorithm establishes the corn LNC inversion model.Comprehensive evaluation of maize LNC inversion model accuracy under different conditions.The results show that the texture information of digital images contributes to the accuracy of model inversion.After the multi-spectral canopy spectral features plus texture information,the R~2 of the RFR,SVR,and DNN models are increased by 0.08,0.13,and 0.09 respectively,the DNN regression model predicts the results of the corn LNC It is better than the RFR and SVR models.Among them,the R~2 of the DNN-F2 model reaches 0.85,the RMSE is 0.27,and the NRMSE is 10.07%.In summary,this paper uses the pixel-level and feature-level fusion of UAV multi-source image feature information by analyzes and selects the spectral and texture features of the corn canopy,and constructs The corn LNC remote sensing quantitative inversion model to achieve the efficient,non-destructive and convenient nitrogen remote sensing monitoring under the multi-source image information fusion of the corn LNC which improves the method of obtaining corn LNC information and provides accurate acquisition of field agricultural information scientific and effective remote sensing technology support.