Detection of crop heights by UAVs based on the Adaptive Kalman Filter

Abstract

Detection of crop heights by UAVs is fast and accurate and can reflect the growth situation of crops. The core of this operation is the measurement of UAV flight altitude. In order to improve the accuracy of the response to the variation of flight altitude, a light-weight altitude detection system was developed for plant-protection UAVs. A millimeter wave radar (MWR) was used as the altitude detector. Moreover, a data fusion algorithm based on the Adaptive Kalman Filter (AKF) was developed. The altitude data by the MWR, the angle data by the Inertial Measurement Unit (IMU) and the position data by Global Positioning System (GPS) were processed by the AKF to obtain optimal updated values, and the optimal updated values was fused to establish the distribution map of crop heights. Results of the trials in the condition of the flight heights of 5 m and 10 m indicated that: 1) compared with the direct detection by the MWR, the error of detection was reduced by 0.035 m.2) compared with real crop heights, the error of detection was 0.02 m. The developed system could achieve the accurate detection of the crop height, providing a new theoretical model and technical idea for the UAVs configured for plant-protection.

Canopy Laser Interception Compensation Mechanism—UAV LiDAR Precise Monitoring Method for Cotton Height

“…However, optical remote sensing data are prone to saturation in areas of high biomass or high LAI (leaf area index) [12] and, therefore, have significant limitations in studies of predicting crop structural parameters, such as vegetation height. This phenomenon can be mitigated to some extent by fusing millimeter-wave radar data, but the overall effect remains unsatisfactory [13]. In addition, digital cameras cannot penetrate the vegetation canopy to obtain ground height data, so accurate crop height estimation generally requires two flying missions, including collecting digital terrain models (DEM) without vegetation cover during sowing and after harvest and digital surface model (DSM) data with crop cover, which greatly reduces the convenience of data acquisition.…”