Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle

doi:10.3390/app10051759

	Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle
	Guo, Han 3; Zhou, Jun 3; Liu, Fei 1,3; He, Yong 1,3; Huang, He 4; Wang, Hongyan 2
刊名	APPLIED SCIENCES-BASEL
	2020-03-01
卷号	10
关键词	UAV spray nozzle spray characteristics machine learning quantitative modeling
DOI	10.3390/app10051759
通讯作者	Liu, Fei(fliu@zju.edu.cn)
英文摘要	Featured Application Progress in Spray Science and Technology. Abstract Unmanned Aerial Vehicle (UAV) spray has been used for efficient and adaptive pesticide applications with its low costs. However, droplet drift is the main problem for UAV spray and will induce pesticide waste and safety concerns. Droplet size and deposition distribution are both highly related to droplet drift and spray effect, which are determined by the nozzle. Therefore, it is necessary to propose an evaluating method for a specific UAV spray nozzles. In this paper, four machine learning methods (REGRESS, least squares support vector machines (LS-SVM), extreme learning machine, and radial basis function neural network (RBFNN)) were applied for quantitatively evaluating one type of UAV spray nozzle (TEEJET XR110015VS), and the case of twin nozzles was investigated. The results showed REGRESS and LS-SVM are good candidates for droplet size evaluation with the coefficient of determination in the calibration set above 0.9 and root means square errors of the prediction set around 2 mu m. RBFNN achieved the best performance for the evaluation of deposition distribution and showed its potential for determining the droplet size of overlapping area. Overall, this study proved the accuracy and efficiency of using the machine learning method for UAV spray nozzle evaluation. Additionally, the study demonstrated the feasibility of using machine learning model to predict the droplet size in the overlapping area of twin nozzles.
资助项目	National Key Research and Development Program of China[2017YFD0701002] ; Key Research and Development Program of Ningxia Hui Autonomous Region[2017BY067]
WOS关键词	UNMANNED AERIAL VEHICLE ; DRIFT ; ATOMIZATION ; PREDICTION ; REGRESSION ; PRESSURE ; SYSTEM
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
语种	英语
出版者	MDPI
WOS记录号	WOS:000525298100207
资助机构	National Key Research and Development Program of China ; Key Research and Development Program of Ningxia Hui Autonomous Region
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/103663]
专题	中国科学院合肥物质科学研究院
通讯作者	Liu, Fei
作者单位	1.Minist Agr & Rural Affairs, Key Lab Spect Sensing, Hangzhou 310058, Peoples R China 2.West China Elect Business Co Ltd, Yinchuan 750002, Ningxia, Peoples R China 3.Zhejiang Univ, Coll Biosyst Engn & Food Sci, 866 Yuhangtang Rd, Hangzhou 310058, Peoples R China 4.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
推荐引用方式 GB/T 7714	Guo, Han,Zhou, Jun,Liu, Fei,et al. Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle[J]. APPLIED SCIENCES-BASEL,2020,10.
APA	Guo, Han,Zhou, Jun,Liu, Fei,He, Yong,Huang, He,&Wang, Hongyan.(2020).Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle.APPLIED SCIENCES-BASEL,10.
MLA	Guo, Han,et al."Application of Machine Learning Method to Quantitatively Evaluate the Droplet Size and Deposition Distribution of the UAV Spray Nozzle".APPLIED SCIENCES-BASEL 10(2020).