Monitoring of Direct-seeded Rice Nitrogen Content Based on UAV Multispectral Images

Wang Ming; Jiang Xun; Gao Longfei; Shi Shengqiao; Li Yanli; Lu Bilin

doi:10.15933/j.cnki.1004-3268.2026.07.016

Abstract：In order to quickly and accurately estimate the nitrogen content in leaves of direct-seeded rice,taking Huanghuazhan,Chunliangyouchang 70 and Liangyou 185 as objects,the DJI Phantom 4 drone was used to obtain the canopy multispectral images of the four periods,tillering stage,jointing stage,booting stage and heading stage of direct-seeded rice.Five vegetation indices with high correlation with leaf nitrogen content were selected respectively,and four algorithms,support vector machine(SVM),random forest(RF),back propagation neural network(BPNN)and partial least squares regression(PLSR)were used to build an estimation model of rice leaf nitrogen content and verify the accuracy.The results showed that there were differences in the correlation between vegetation index and leaf nitrogen content(CNC)of rice plants at different growth stages.The correlation between enhance vegetation index(EVI)and leaf nitrogen content at tillering stage was higher.The correlation coefficient was 0.858.The correlation coefficient between ratio vegetation index(RVI)and leaf nitrogen content was higher at jointing stage(0.938),the correlation coefficient between RVI and leaf nitrogen content was higher at booting stage(0.793),the NDVI and SAVI indices at the heading stage showed a higher correlation with leaf nitrogen content,with a correlation coefficient of 0.782.The accuracy of the models at tillering stage and jointing stage was higher,while the accuracy of the models at booting stage and heading stage was lower,and the inversion effect was poor.The prediction model of rice leaf nitrogen content based on the RF algorithm performed relatively better,and the Rc2 was 0.922,RMSEc was 0.101%,Rcv2 was 0.658,and RMSEcv was 0.205%at tillering stage.At jointing stage,Rc2 was 0.980,RMSEc was 0.061%,Rcv2 was 0.913,and RMSEcv was 0.124%.The results showed that the leaf nitrogen content monitoring model based on RF had high prediction accuracy and could predict the leaf nitrogen content of direct-seeded rice.