Zhu Sun

Recently, multi-rotor unmanned aerial vehicle (UAV) becomes more and more significantly irreplaceable in the field of plant protection against diseases, pests and weeds of crops. The easy takeoff and landing performance, hover function and high spraying efficiency of UAV are urgently required to spray pesticide for crop timely and effectively, especially in dispersed plots and hilly mountains. In such situations, the current researches about UAV spray application mainly focus on studying the influence of the UAV spraying parameters on the droplet deposition, such as operation height, operation velocity and wind velocity. The deposition and distribution of pesticide droplets on crops which depends on installation position of nozzle and airflow distribution characteristics of UAV are directly related to the control effect of pesticide and crop growth in different growth periods. As a preliminary step, this study focuses on the dynamic development law and distribution characteristics of the downwash air flow for the SLK-5 six-rotor agricultural UAV. Based on compressible Reynolds-averaged Navier-Stokes (RANS) equations with an RNG k-ε turbulence model and dynamic mesh technology, the efficient three-dimensional computational fluid dynamics (CFD) method was established to analyze the flow field distribution characteristics of UAV in hover. Then the unsteady interaction flow field of the wing was investigated in detail. The downwash wind speed of the marked points for the SLK-5 UAV in hover was also tested by weather tracker. It was found that the maximum velocity value of the downwash flow was close to 10 m/s; the z-direction velocity was the main body of the wind velocity in the downwash airflow, and the comparison of the wind velocity experiment test and simulation showed that the relative error was less than 12% between the experimental and simulated values of the z-direction velocity at the marked points. Then the flow characteristics of the longitudinal and cross section were analyzed in detail, the results obtained can be used as a reference for drift and sedimentation studies for multi-rotor unmanned aerial vehicle. Keywords: UAV, downwash air flow, numerical simulation, experimental verification, pesticide spray, wing interference DOI: 10.25165/j.ijabe.20171004.3077 Citation: Yang F B, Xue X Y, Zhang L, Sun Z. Numerical simulation and experimental verification on downwash air flow of six-rotor agricultural unmanned aerial vehicle in hover. Int J Agric & Biol Eng, 2017; 10(4): 41–53.

The effective coverage and velocity of downwash are directly related to the assemblage of spraying system and spraying effect. The downwash of the unmanned agricultural helicopter (UAH) N-3 was discussed in the paper. The computational fluid dynamics (CFD) methods were used to simulate and analyze the distribution of the downwash, and a wind field measurement device had been designed to test the downwash of UAH N-3. In the tests, the UAH N-3 was raised up to 5.0 m, 6.0 m and 7.0 m from the ground, “annular-radial-distribution- point” method was introduced, 8 directions separated by an angle of 45° (the radial direction) with the intersection point of the main rotor shaft and the ground plane as the center, 0.5 m as the step length for the longitudinal (to 2.5 m) and radial (to 4.0 m) direction to set the sample points, considering the range of the rotor rotating circular area mainly. The 5 m height results of N-3 were fully discussed to describe the downwash distribution with the longitudinal altitude increased and the radial distance increased. The standard deviations of five test altitudes for eight directions were comparatively analyzed, the results showed that the total standard deviation was not greater than 0.6 m/s. The overall relative maximum margin of error calculated from the simulation and measurement data was between 0.6 and 0.7, which verified the credibility of the simulation data. High-order polynomials were used to fitting the simulation and measurement data, the fitting results showed that the polynomial coefficient of determination R2 met or exceeded 0.75 when the altitudes were more than 1 m, indicating the fit equation having the reference values. When the altitudes equal or less than 0.5 m, the polynomial coefficient of determination R2 was smaller, ranging during 0.3 to 0.7. The study would provide some foundations for the optimization of the assemblage of spraying system on the single-rotor UAH, which would promote China aviation plant protection. Keywords: unmanned agricultural helicopter, single rotor, CFD simulation, downwash distribution, spraying effect DOI: 10.25165/j.ijabe.20171005.3079 Citation: Zhang S C, Xue X Y, Sun Z, Zhou L X, Jin Y K. Downwash distribution of single-rotor unmanned agricultural helicopter on hovering state. Int J Agric & Biol Eng, 2017; 10(5): 14–24.

In this article, we propose a fuzzy proportional–integral–derivative (Fuzzy-PID) controller that integrates a system-identification-based control strategy. We aim to address the challenge of regulating electrical conductivity (EC) in a fertigation system to ensure precise nutrient delivery. During fertilization, the nutrient solution EC value increases gradually and nonlinearly as water and fertilizer are integrated. Precise fertilizer injection is essential to maintain stable EC levels, preventing crop undernutrition or overnutrition. The fertigation process is modeled using a particle swarm optimization (PSO)-based system identification method. A Fuzzy-PID method is then employed to regulate the nutrient solution EC value based on the pre-determined or real-time identified transfer model. The proposed control strategy is deployed within a programmable logic controller (PLC) environment and validated on a PLC-based fertilizer system. The results show that the identified transfer model accurately represents the fertilizer mixing process, achieving a standard Mean Absolute Percentage Error (MAPE) value of less than 5% within 2 s using the proposed PSO-based identification method. In the simulation tests, the proposed Fuzzy-PID control rule would converge the nutrient solution to target EC values 1000 and 1500 μs/cm within a deviation band ± 50 μs/cm, within 6 s from the recorded identified transfer models and within 25 s from the real-time identified transfer models. In the device’s test, the convergence time of the fertigation EC control is approximately 16 s from the history data and 42 s from the real-time collected data, with a deviation band ± 50 μs/cm. In contrast, it may take over 70 s for the EC regulation of the same fertilization, using the classic control methods including conventional PID and Fuzzy-PID. The proposed control strategy significantly improves EC regulation in terms of speed, stability, and precision, enhancing the performance of fertilizer mixing systems.

Application of Unmanned Aircraft Systems (UAS) for plant protection is becoming a common tool in agricultural field management. To avoid shortcomings of intrusive flowrate sensors including poor measurement accuracy and poor anti-vibration ability, a non-intrusive flowrate measurement and monitoring system of plant-protection UAS was developed based on pump voice signal analysis. It is mainly composed of STM32 processor, microphone and signal-conditioning circuit. By collecting and analyzing the voice signal of the pump in the UAS, the monitoring system will output the real-time values of spraying flowrate and amount. An extraction model was developed to determine operation status and primary frequency of the pump based on voice signal analysis. Real-time spray flowrate can be determined from the real-time extracted primary frequency and the fitted correlation formulas of spraying flowrate under outlet area and pump primary frequency. The flowrate correlation equation of one certain pump from 4-rotor UAS 3WQFTX-1011S was obtained, the max deviation rate of fitted spray flowrate was only 2.8%. In primary frequency extraction test, the error rate of primary frequency extraction was less than 1%. In the 4-rotor UAS flight tests: the max deviation of operating starting/end point was only 0.7 s and the max deviation of extracted total operating time was only 0.8 s; the deviation of extracted spray flowrate was less than 2%, and the max deviation rate of total spray amount was 3.2%. This research could be used as a guidance for plant-protection UAS non-intrusive flowrate measurement and monitoring. Keywords: plant protection UAS, voice signal analysis, non-intrusive, flowrate measurement, monitoring system DOI: 10.25165/j.ijabe.20211403.5508 Citation: Xu Y, Xue X Y, Sun Zh, Gu W. Non-intrusive flowrate measurement and monitoring system of plant-protection unmanned aircraft systems based on pump voice analysis. Int J Agric & Biol Eng, 2021; 14(3): 58–65.