Nitrogen Status Monitoring in Rice Based on Unmanned Aerial Vehicles (UAV).

Abstract: Image-based remote sensing is a promising technique for precision crop management. This study aims at identifying the use of an Unmanned Aerial System for monitoring rice (Oryza sativa L.) nitrogen (N) status under different conditions. The Unmanned Aerial System was composed of a 6-Band Multispectral camera and an eight-rotor Oktokopter, and the rice experiment was conducted with two rice cultivars under two planting densities (30*15cm/50*15cm) and four N rates (0, 100, 200 and 300 kg/ha) at Jiangsu, China. Five UAV flights, coupled with ground field measurements, were carried out during the whole rice growth period. Six spectral indices were calculated with several available wavebands from the 6 bands camera: the normalized difference vegetation index (NDVI) and ratio vegetation index (RVI), red edge chlorophyll index (CIred edge), green chlorophyll index (CIgreen), the modified chlorophyll absorption ratio index/optimized soil-adjusted vegetation index (MCARI/OSAVI), and the triangular chlorophyll absorption ratio index/optimized soil-adjusted vegetation index (TCARI/OSAVI). The relationships were analyzed between six spectral indices and two N variables: leaf N concentration (LNC) and leaf N accumulation (LNA). Results indicated that the RVI_{(800, 720)} showed the highest correlation (R²=0.66) with LNC, independently of the planting densities and the N rate. RVI_(800,550) was most correlated with LNA value (R²=0.69). However, the regression coefficients were dependent on growth stages. In order to improve the monitoring accuracy, we added the transplanting date into the established model through the method of multiple linear regression. The correlation between RVI_{(800, 720)} and LNC and between RVI_{(800, 550)} and LNA were improved to 0.75 and 0.72 during the whole growing stage, respectively. These results lay a foundation for the promising application of UAV-based system on rice N monitoring.

Keywords: 6-Band Multispectral camera, Nitrogen status, Rice, Spectral index, Unmanned Aerial Vehicle (UAV)