Detection of Rape Canopy SPAD Based on Multispectral Images of Low Altitude Remote Sensing Platform

Abstract. Nutrient detection and distribution visualization of rapeseed canopy by low-altitude multi-spectral remote sensing is very important for precision nutrient management. In this research, a multi-spectral camera was used on the unmanned aerial vehicles (UAV) low-level remote sensing simulation platform to obtain multi-spectral images of rape canopy at seedling stage and six vegetation indices were selected for optimization, and a linear analytical model of canopy SPAD was established based on the vegetation indices. The influence of image acquisition height and velocity on the model prediction was further analyzed by setting different camera heights and velocities. The results show that the prediction performance of the model may be improved to different extents with the increase of image acquisition height and reduction of image acquisition velocity and SPAD linear prediction model based on vegetation index (NIR-G)/(NIR+G) is optimal when the camera velocity is 0.1m/s and height is 1.9m, the correlation coefficient Rp is up to 0.7354. This research lays a theoretical foundation for rapidly obtaining the large-scale rape canopy nitrogen information via UAV platform-based low altitude multi-spectral remote sensing technology in the future.