Monitoring Of Main Agronomic Indices Of Oat In Coastal Salt Soil Area Based On Near-ground Hyperspectral Data

China is a big country of oats,and the planting area of oats is at the forefront of the world Oats are an important food ration for Chinese people and also a good salt-tolerant crop.However,the acceleration of urbanization is gradually reducing the arable land area,and China is one of the countries plagued by salinization.Land salinization further reduces the available arable land area,so the use of coastal salinization to grow oat has attracted increasing attention.In recent years,with the rapid development of information technology,new diagnostic methods,new application techniques and new research fields have gradually become the focus of research.Hyperspectral technology as a kind of nondestructive determination of N content in crops has attracted much attention.The change of plant total nitrogen content leads to the change of crop growth index and physiological index,and then leads to the change of spectral reflection characteristic curve of crop.A great deal of progress has been made in the study of spectral variation characteristics and nitrogen content at home and abroad.The main achievements include the study of the sensitive band of plant nitrogen content and the combination of the corresponding vegetation index,and the establishment of the prediction model of plant nitrogen content for a variety of hyperspectra.In this study,oat was used as the research object.Based on the field experiments under different fertilizers and varieties,the vegetation index of main growth period of oat was extracted by ASD hyperspectral method,and the biomass and yield of oat were obtained by simultaneous destructive sampling.At the same time analysis index of different color and texture feature parameters and biomass and yield of oats,select suitable characteristic parameters of oat growth index,physiological index build based on unmanned aerial vehicle(uav)image of oats,plant height,leaf area index estimating model of total nitrogen content and SPAD values of for condition monitoring and yield estimation oats provide effective technical support.The main conclusions are as follows(1)In order to build models for predicting the oats plant total nitrogen content,combined with fertilization species and different varieties of test,this study analyzed the vegetation index and the main growth period of plant total nitrogen content of correlation,and separate the main growth period was established based on various periods of data models for predicting the total nitrogen content of oat,the results show that the jointing stage and booting stage and filling stage of vegetation index and the correlation of oats plant total nitrogen content reached significant level,SAVI-NPCI booting stage of vegetation index and oats plant total nitrogen content in the highest correlation;In the jointing stage,the correlation between vegetation index REIP1@NDRE and total nitrogen content of oat plants was high,and the R2 reached 0.7708.The correlation between vegetation index and total nitrogen content of oat plants at booting stage was the best,and the coefficient of determination between vegetation index SAVI-NPCI and total nitrogen content of oat plants reached 0.8442.The correlation between NDVI750/650/RVI and total nitrogen content of oat plants decreased,and the coefficient of determination between NDVI750/650/RVI and total nitrogen content of oat plants reached the highest value of 0.5305.In order to construct the best estimation model,a multivariate index composed of different combinations of vegetation index was proposed to construct an estimation model of total nitrogen content in oat plants.The results indicate that the correlation more than 0.5 with the RMSE also keeping a small range during the modeling process.Finally,the estimated values were validated by the measured values of total nitrogen content of oat plants.The R2 values verified by the model were all above 0.5,and the RMSE was small.(2)In order to build models for predicting the oat SPAD value,between different wheat varieties and different treatments were based on the test,analyzing the individual growth period of vegetation index and the oat leaf SPAD value of correlation,and separate use of the individual to evaluate the data on oat SPAD value growth period,the results show that the filling stage MCARI-CARI combination of vegetation index and leaf SPAD value has the highest correlation,and grouting phase correlation is low.NPCI-SAVI had the best correlation with leaf SPAD value,and the determination coefficient R2 was 0.7521.The combined vegetation index NPCI-SAVI was used to estimate the SPAD value of oat.The determination coefficient R2 of the model was 0.7465,the root mean square error RMSE was 4.92,and the absolute error RE(%)was 11.73%,indicating that the model could be used.MTCI/NDRE at booting stage was also used to estimate the SPAD value of oat,with a determination coefficient R2 of 0.7019.Finally,the model was verified by measuring the SPAD value of oat at booting stage.The determination coefficient R2 of model validation was 0.7013,the root mean square error(RMSE)was 4.518,and the absolute error RE(%)was 11.73%,indicating that the model could be used.(3)In order to build models for predicting the oats plant height,based on different varieties of oat field experiment,fertilization sort,analyzes the individual reproductive period the relation between vegetation index and oats plant height,and use a single individual growth period data of oats plant height were estimated,the results showed that elongation stage correlation is best,and low relevance between booting stage and filling stage.The correlation of REIP1 in jointing stage was the best,and R2 was 0.844.Using REIP1 at jointing stage to estimate the height of oat plant had a good effect,and using REIP1 at booting stage to estimate the height of oat plant had a good correlation of-0.792,which reached a very significant level.Finally,the estimated value of the oat SPAD value was verified by the model.In the modeling process,the determination coefficient R2 was 0.6244,the RMSE was 2.087(cm),and the root mean square error RE(%)was 11.76%.The model was available.The relationship between the combined vegetation index MCARI/SAVI and plant height was good.The determination coefficient R2 of the model was 0.5825,the determination coefficient R2 of the validation model was 0.5613,the root mean square error(RMSE)was 0.919(cm),and the relative error Re(%)was 7.61%.The correlation between vegetation index(REIP1+NDVI750/650)/(REIP1-NDVI750/650)REIP1+NDVI750/650 and plant height in the grain-filling stage was good,and the coefficient of determination(R2)was 0.6252,and the coefficient of determination(R2)of the validation model was 0.5415.The root mean square error(RMSE)was 1.211(cm),and the absolute error RE(%)was 2.05%.(4)In order to build oat leaf area index(LAI)models for predicting the value,to the oat field experiment based on different varieties,fertilizer varieties,single birth period,vegetation index and oats were analyzed the correlation between leaf area index,and use a single individual reproductive period of the data to estimate oat leaf area index,the results showed that the elongation stage correlation is best,and the correlation of booting stage and filling stage is low.The correlation between leaf area index(LAI)and vegetation index(NDRE-MTCI)at jointing stage was the best,and the coefficient of determination(R2)was 0.4085.The determination coefficient(R2),root mean square error(RMSE)and absolute error(RE%)of Oat leaf area index(LAI)validation model constructed by NDRE-MTCI at joinning stage were 0.4005,0.071,and 13.91%.The estimation of Oat leaf area index(LAI)by Cari-McAri at the boot stage also had a good effect,with the coefficient of determination R2 being 0.6604,the coefficient of determination R2 of the validation model being 0.653,the root mean square error(RMSE)being 0.012,and the absolute error(RE%)being 2.79%.The model could be used.At the grain-filling stage,the REIP1/RVI determination coefficient was 0.584,the determination coefficient R2of the validation model was 0.532,the root mean square error(RMSE)was 0.1360,and the absolute error(RE%)was 16.47%.The model was available.The R2 of each growth stage exceeded 0.5 during the modeling process.Finally,the estimated value of oat SPAD was verified by the measured values in different periods,and the R2 of model verification was above 0.5,and the RMSE was small.