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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7831310/ https://www.ncbi.nlm.nih.gov/pubmed/33477600 http://dx.doi.org/10.3390/s21020638 |
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author | Ni, Ming Wang, Hongjie Liu, Xudong Liao, Yilin Fu, Lin Wu, Qianqian Mu, Jiong Chen, Xiaoyan Li, Jun |
author_facet | Ni, Ming Wang, Hongjie Liu, Xudong Liao, Yilin Fu, Lin Wu, Qianqian Mu, Jiong Chen, Xiaoyan Li, Jun |
author_sort | Ni, Ming |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-7831310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78313102021-01-26 Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis Ni, Ming Wang, Hongjie Liu, Xudong Liao, Yilin Fu, Lin Wu, Qianqian Mu, Jiong Chen, Xiaoyan Li, Jun Sensors (Basel) Article 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. MDPI 2021-01-18 /pmc/articles/PMC7831310/ /pubmed/33477600 http://dx.doi.org/10.3390/s21020638 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ni, Ming Wang, Hongjie Liu, Xudong Liao, Yilin Fu, Lin Wu, Qianqian Mu, Jiong Chen, Xiaoyan Li, Jun Design of Variable Spray System for Plant Protection UAV Based on CFD Simulation and Regression Analysis |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7831310/ https://www.ncbi.nlm.nih.gov/pubmed/33477600 http://dx.doi.org/10.3390/s21020638 |
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