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 µm. 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.
Highlights
Comparing with other pesticide applicators, unmanned aerial vehicles (UAV) can achieve accurate and specific management with its low costs, high efficiency, and mobility [1,2]
The volume medium diameter (VMD) distribution was shown in Figure 5; standard deviations were labeled as error bars; the VMD values of XR110015VS nozzle were increased with the horizontal position and spray height
According to the Jet theory in the cross-flow environment, the droplet sector spreading with the According the Jet theory the cross-flow environment, droplet sector with the increase of spray to height, more andinmore small droplets drift awaythe from the spray fanspreading with the velocity height, air more more small droplets away from of theVMD
Summary
Comparing with other pesticide applicators, unmanned aerial vehicles (UAV) can achieve accurate and specific management with its low costs, high efficiency, and mobility [1,2]. As far as we know, machine learning methods haven’t been widely investigated in quantitative modeling of the atomization performance of nozzle used for UAV spray. The droplet size and deposition distribution in the overlapping area of multi-nozzle spray should be investigated. We took TEEJET XR110015VS nozzle as the object and aimed to explore the reliability of using the machine learning method to quantitatively evaluate droplet size and deposition distribution for TEEJET XR110015VS nozzle and twin nozzles condition. This study was the first time to apply machine learning methods to the quantitative evaluation of the droplet size of the UAV spray nozzle, and the reliability of these methods was proved.
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