Monitoring Growth Status and Predicting Grain Yield in Wheat Based on the Platform of Unmanned Aerial Vehicle and Active Canopy Sensor.

Using unmanned aerial vehicle (UAV) remote sensing monitoring system can rapidly and cost-effectively provide canopy growth information for crop management. In order to large-scale monitor nitrogen nutrition and predict grain yield in wheat, field experiment was carried out with different wheat varieties and nitrogen fertilizer treatments in 2016. Two types of canopy spectral data were measured synchronously by RapidSCAN CS-45, an active canopy sensor, mounted on the platform of multi-rotor UAV and handheld platform. At key growth stages, the growth indices (shoot biomass, leaf area index), plant nitrogen accumulation and grain yield of wheat at each plots were obtained. The relationship between UAV-based and handheld-based canopy data was analyzed and compared, a quick method for acquiring canopy spectral data has been developed based on UAV remote sensing. We analyzed the quantitative relationships of UAV-based canopy spectral reflectance and vegetation indices with agronomic indices (i.e. nitrogen content, leaf area index), the most sensitive spectral bands and vegetation indices were selected to establish non-destructive estimation model for wheat nitrogen nutrition and yield prediction model based on UAV remote sensing. Single-stage and duration models were developed to diagnose N status and estimate yield potential. The duration models indicated that normalized difference red edge index (NDRE) explained 78.4%, 70.9% and 85.2% of LAI, shoot biomass and plant N accumulation, respectively. The NDRE at heading stage explained 88% of the variability in grain yield of wheat. These results demonstrate that UAV-based active canopy sensor can be used to monitor growth status and predict grain yield of wheat.

Key words: wheat; unmanned aerial vehicle; RapidScan; growth index; nitrogen nutrition; grain yield