Development of a low-cost quadrotor UAV based on ADRC for agricultural remote sensing.

Unmanned aerial vehicle (UAV) has the advantages of good repeatability and high remote sensing (RS) information acquisition efficiency, as an important supplement bridging the gap of high-altitude and ground RS platforms. A quadrotor UAV was developed for the agricultural RS application in this study. The control system consists of a main processor and a coprocessor, integrating a three-axis gyroscope, a three-axis accelerometer, an air pressure sensor and a global positioning system (GPS) module. Engineering trial method (ETM) was used to tune the parameters based on the active disturbance rejection control (ADRC) method. Also a ground control station (GCS) adapted to the quadrotor was developed realizing autonomously take-off and landing, flight route planning, data recording. To investigate the performances of the UAV, several flight tests were carried out. The test results showed that the pitch angle control accuracy error was less than 4°, the flight height control accuracy error was less than 0.86 m, the flight path control accuracy error was less than 1.5 m overall. Aerial multispectral images were acquired and processed. The reflected digital number (DN) values obtained from a height of 10-100 m with 10 m interval could be referenced to classify objects. The normalized-difference-vegetation index (NDVI) values obtained from the aerial multispectral images acquired at 15 m were compared with those obtained by the GreenSeeker (GS) and PSR-1100F. The maximum error was 20.37% while the minimum error was 1.99%, which demonstrated the developed quadrotor UAV’s satisfactions for low altitude remote sensing practice. This study provided a low-cost platform for agricultural remote sensing. Keywords: UAV, quadrotor, ADRC, agricultural remote sensing, NDVI, aerial spraying application DOI: 10.25165/j.ijabe.20191204.4641 Citation: Zhang S C, Xue X Y, Chen C, Sun Z, Sun T. Development of a low-cost quadrotor UAV based on ADRC for agricultural remote sensing. Int J Agric & Biol Eng, 2019; 12(4): 82–87.

[1]  Frédéric Baret,et al.  Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots , 2008, Sensors.

[2]  Kai-Yuan Cai,et al.  Tracking control for a velocity-sensorless VTOL aircraft with delayed outputs , 2009, Autom..

[3]  Nikrooz Bagheri,et al.  Development of a high-resolution aerial remote-sensing system for precision agriculture , 2017 .

[4]  Zhang Weiguo,et al.  Robust trajectory tracking for quadrotor aircraft based on ADRC attitude decoupling control , 2015 .

[5]  Kristof Van Oost,et al.  UAS-based soil carbon mapping using VIS-NIR (480–1000nm) multi-spectral imaging: Potential and limitations , 2016 .

[6]  Zhang Huihui,et al.  Drift and deposition of ultra-low altitude and low volume application in paddy field , 2014 .

[7]  Chun Chang,et al.  Develop an unmanned aerial vehicle based automatic aerial spraying system , 2016, Comput. Electron. Agric..

[8]  Alessandro Matese,et al.  A flexible unmanned aerial vehicle for precision agriculture , 2012, Precision Agriculture.

[9]  R. Lucas,et al.  The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data , 2006 .

[10]  Yubin Lan,et al.  Development and prospect of unmanned aerial vehicle technologies for agricultural production management , 2013 .

[11]  Han Shuaitao.  Attitude Decoupling Control for Quadrotor Aircraft Based on Active Disturbance Rejection Control Technique , 2013 .

[12]  D. Lamb.  The use of qualitative airborne multispectral imaging for managing agricultural crops : a case study in south-eastern australia , 2000 .

[13]  Zhu Li-ling.  Dynamic parameter setting and application of active disturbance rejection controller , 2005 .

[14]  Matthew Bardeen,et al.  Detection and Segmentation of Vine Canopy in Ultra-High Spatial Resolution RGB Imagery Obtained from Unmanned Aerial Vehicle (UAV): A Case Study in a Commercial Vineyard , 2017, Remote. Sens..

[15]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[16]  F. A. Vega,et al.  Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop , 2015 .

[17]  Won Suk Lee,et al.  Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees , 2013 .

[18]  Han Jing,et al.  From PID Technique to Active Disturbances Rejection Control Technique , 2002 .