Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis
Multi-rotor unmanned aerial vehicles (UAVs) for plant protection are widely used in China’s agricultural production. However, spray droplets often drift and distribute nonuniformly, thereby harming its utilization and the environment. A variable spray system is designed, discussed, and verified to s...
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doaj-a6ac73dba2c94dd8abe426dc74ed02962021-01-19T00:03:32ZengMDPI AGSensors1424-82202021-01-012163863810.3390/s21020638Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression AnalysisMing Ni0Hongjie Wang1Xudong Liu2Yilin Liao3Lin Fu4Qianqian Wu5Jiong Mu6Xiaoyan Chen7Jun Li8College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Science, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaMulti-rotor unmanned aerial vehicles (UAVs) for plant protection are widely used in China’s agricultural production. However, spray droplets often drift and distribute nonuniformly, thereby harming its utilization and the environment. A variable spray system is designed, discussed, and verified to solve this problem. The distribution characteristics of droplet deposition under different spray states (flight state, environment state, nozzle state) are obtained through computational fluid dynamics simulation. In the verification experiment, the wind velocity error of most sample points is less than 1 m/s, and the deposition ratio error is less than 10%, indicating that the simulation is reliable. A simulation data set is used to train support vector regression and back propagation neural network with multiple parameters. An optimal regression model with the root mean square error of 6.5% is selected. The UAV offset and nozzle flow of the variable spray system can be obtained in accordance with the current spray state by multi-sensor fusion and the predicted deposition distribution characteristics. The farmland experiment shows that the deposition volume error between the prediction and experiment is within 30%, thereby proving the effectiveness of the system. This article provides a reference for the improvement of UAV intelligent spray system.https://www.mdpi.com/1424-8220/21/2/638aviation plant protectiondownwash wind fielddeposition distribution characteristicsupport vector regressionback propagation neural networkfarmland experiment |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ming Ni Hongjie Wang Xudong Liu Yilin Liao Lin Fu Qianqian Wu Jiong Mu Xiaoyan Chen Jun Li |
spellingShingle |
Ming Ni Hongjie Wang Xudong Liu Yilin Liao Lin Fu Qianqian Wu Jiong Mu Xiaoyan Chen Jun Li Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis Sensors aviation plant protection downwash wind field deposition distribution characteristic support vector regression back propagation neural network farmland experiment |
author_facet |
Ming Ni Hongjie Wang Xudong Liu Yilin Liao Lin Fu Qianqian Wu Jiong Mu Xiaoyan Chen Jun Li |
author_sort |
Ming Ni |
title |
Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis |
title_short |
Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis |
title_full |
Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis |
title_fullStr |
Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis |
title_full_unstemmed |
Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis |
title_sort |
design of variable spray system for plant protection uav based on cfd simulation and regression analysis |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-01-01 |
description |
Multi-rotor unmanned aerial vehicles (UAVs) for plant protection are widely used in China’s agricultural production. However, spray droplets often drift and distribute nonuniformly, thereby harming its utilization and the environment. A variable spray system is designed, discussed, and verified to solve this problem. The distribution characteristics of droplet deposition under different spray states (flight state, environment state, nozzle state) are obtained through computational fluid dynamics simulation. In the verification experiment, the wind velocity error of most sample points is less than 1 m/s, and the deposition ratio error is less than 10%, indicating that the simulation is reliable. A simulation data set is used to train support vector regression and back propagation neural network with multiple parameters. An optimal regression model with the root mean square error of 6.5% is selected. The UAV offset and nozzle flow of the variable spray system can be obtained in accordance with the current spray state by multi-sensor fusion and the predicted deposition distribution characteristics. The farmland experiment shows that the deposition volume error between the prediction and experiment is within 30%, thereby proving the effectiveness of the system. This article provides a reference for the improvement of UAV intelligent spray system. |
topic |
aviation plant protection downwash wind field deposition distribution characteristic support vector regression back propagation neural network farmland experiment |
url |
https://www.mdpi.com/1424-8220/21/2/638 |
work_keys_str_mv |
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