Design and testing of a crop growth sensor aboard a fixed-wing unmanned aerial vehicle

Abstract

• A set of crop growth sensors mounted on a fixed-wing UAV were developed. The system can obtain crop canopies and growth indices, such as LNC and LAI, on-line and in a convenient, high-throughput manner while ensuring that theUAV's airflow does not disturb the crop canopies below. • Flight dynamics computer simulation analysis of the fixed-wing UAV was conducted by the ADAMS software to obtain the deflection angle of the UAV during flight. A ball rolling-type sensor support was designed to ensure that the crop growth sensor is always aimed vertically downwards in-flight. • Due to the adjustment of the sensor support, the system has good dynamic stability and measurement accuracy within the working height range of the sensor. The aim of this study is to overcome disturbance of downwash flow field caused by the low-altitude operation of a multirotor unmanned aerial vehicle (UAV) on crop canopies and interference in spectral reflection information of canopies. For this purpose, a crop growth sensor aboard a fixed-wing UAV was developed through flight dynamics simulation analysis of a fixed-wing UAV. This sensor can collect index data on-line and in real-time including: the ratio vegetation index (RVI) of crop leaves, leaf area index (LAI), leaf dry weight (LDW), and leaf nitrogen content (LNC). Flight dynamics simulation analysis of the fixed-wing UAV was conducted by the automatics dynamic analysis of mechanical system (ADAMS) software to obtain the deflection angle of the UAV during flight. According to the flight characteristics and load on the UAV, a ball rolling-type sensor support was designed to ensure that the crop growth sensor is always aimed vertically downwards in-flight. The field test results show that the crop growth sensor aboard the fixed-wing UAV has good dynamic stability and high measurement accuracy. The RVIs measured by the onboard crop growth sensor in the plots and field were fitted with the results measured by a FieldSpec HandHeld 2 spectroradiometer (ASD, Analytical Spectral Device Co., USA). By analysing the fitted results, the coefficients of determination ( R 2 ) are 0.763 and 0.833 and the root mean square errors (RMSEs) are 0.16 and 0.17, respectively. By linearly fitting RVIs measured by the UAV with rice growth indices including LAI, LDW, and LNC, the coefficients of determination ( R 2 ) are 0.633, 0.581, and 0.528 and RMSEs are 0.18, 0.18, and 0.21, respectively.