Construction And Validation Of Monitoring Model For Leaf Nitrogen And Chlorophyll Content Of Jinsha Pomelo

Jinsha pomelo（Citrus grandis（L.）Osbeck）is one of the high-quality honey pomelo varieties planted in Ji’an city,Jiangxi province,China.With the continuous increase of planting area and output,it has become the leading industry for local fruit farmers to increase income and get rich.Nitrogen deficit directly affects the growth and development of Jinsha pomelo and the formation of yield and quality.Excessive application of nitrogen fertilizer leads to the reduction of yield and quality of Jinsha pomelo,and the aggravation of environmental pollution.Real-time nondestructive monitoring of nitrogen nutrition of Jinsha pomelo leaves and scientific control of nitrogen application is one of the important ways to achieve high yield,quality and efficiency of Jinsha pomelo.With the advantages of fast,accurate,real-time and non-destructive,the spectral remote sensing monitoring technology can monitor the nitrogen nutrition status of Jinsha pomelo,and guide the precise fertilization of Jinsha pomelo.Therefore,this study took Jinsha pomelo as the research object,conducted orchard experiments with different nitrogen application levels,collected canopy spectral reflectance by portable pomelo spectrometer（PPS）,ASD FH2high spectrometer and Rapid SCAN spectrometer,and calculated normalized difference vegetation index（NDVI）and normalized difference red edge（NDRE）.Compare and analyze the measuring accuracy of different spectral monitors,and the monitoring model of leaf chlorophyll content（LCC）and leaf nitrogen content（LNC）in Jinsha pomelo was established based on portable pomelo spectrometer（PPS）.Meanwhile,unmanned aerial vehicle（UAV）RGB camera is used to obtain canopy RGB images of Jinsha pomelo,extract different types of image features（color features,vegetation index,texture features）,and analyze the correlation between different image features and LCC in different growth stages under the region of interest（ROI）of leaves.Establish a monitoring model of leaf chlorophyll content of Jinsha pomelo based on UAV RGB images.So as to realize the rapid non-destructive monitoring of LCC and LNC of Jinsha pomelo,and provide technical support for the rapid non-destructive acquisition of growth index information of Jinsha pomelo and the accurate management of nitrogen fertilizer.The main research results were as follows:（1）A monitoring model of leaf nitrogen content in Jinsha pomelo based on portable pomelo spectrometer（PPS）was established.The results showed that there was no significant difference between NDVI and NDRE collected by portable pomelo spectrometer（PPS）,ASD high altitude spectrometer and Rapid SCAN spectrometer,and the portable pomelo spectrometer（PPS）has a high consistency.Through data fitting analysis,it was found that the correlation between NDVI_PPSand LNC was higher than that between NDRE_PPSand LNC.The power function model based on NDVI_PPScould be used to accurate estimate LCC with the root mean square error（RMSE）,relation root mean square error（RRMSE）and correlation coefficient（r）of model validation in the range of0.1%-0.2%,4.4%-8.1%and 0.8952-0.9333,respectively.（2）A monitoring model of leaf chlorophyll content of Jinsha pomelo based on portable honey pomelo spectrometer（PPS）was established with NDVI polynomial function.The results showed that there was no significant difference between NDVI and NDRE collected by portable pomelo spectrometer（PPS）,ASD hyperspectral spectrometer and Rapid SCAN spectrometer,and the portable pomelo spectrometer（PPS）has a high consistency.Through data fitting analysis,it was found that the correlation between NDVI_PPSand LNC was higher than that between NDRE_PPSand LNC.The polynomial model based on NDVI_PPScould be used to accurate estimate LCC with the root mean square error（RMSE）,relation root mean square error（RRMSE）and correlation coefficient（r）of model validation in the range of 0.07%～0.19%,2.8%～9.0%and 0.8725～0.9912,respectively.（3）A monitoring model of leaves chlorophyll content of Jinsha pomelo was established based on the excess red vegetation index（Ex R）of UAV RGB images.The results showed that the UAV RGB image features in the leaf ROI mode,the highest correlation with the leaf chlorophyll content of Jinsha pomelo was the excess red vegetation index（Ex R）,which could more accurately monitor the LCC of pomelo on a larger scale.The normalized root mean square error（n RMSE）and bias of the model test were 0.15～0.24 and-0.09～0.04,respectively.