# Nozzle with a Feedback Channel for Agricultural Drones
## 3 Experiment Methods
### Measurement of Spraying Performance  
Figure 3 shows a schematic diagram of the experimental setup used to measure the spraying performance. The main flow was injected by using an air pump (Wob-L Piston Gas Pump WA80 DC, YLKTECH), and the supply of water through the additional channel was controlled through hydrostatic pressure. After generating a main flow of 8.1 L/min at the main inlet by using an air pump, spraying was performed by applying hydrostatic pressure. For changing the hydrostatic pressure, a weight was added to the upper part of the water piston, and the spraying performance was confirmed by changing the weight from 0 to 50 kg, in 10 kg increments. The static pressure was measured at the point indicated in Figure 3. The pressure and flow rates corresponding to different loads on the water piston are summarized in Figure 4. Without load, the pressure and flow rates were 0.05 bar and 50 mL/min, respectively. The pressure and flow rates are found to increase with increasing loads. The rate of change is found to increase further for loads heavier than 30 kg. When the load was higher than 30 kg, as the load increased, both the pressure and flow rate increased.  
Figure 3: Schematic of the experimental setup for measuring the spraying performance.  
The images of spraying particles were captured at 2000 frames per second, with a high-speed camera (Fastcam Mini UX50, Photron, San Diego, CA, USA). The obtained images were analyzed, using a digital image-processing technique. The origin was set at the center of the nozzle exit in the analysis, and the \(x\)-axis and \(z\)-axis represent the radial and spraying directions, respectively. As shown in Figure 3, to confirm the distribution of particles over a wide range, spraying water droplets were visualized in four areas and photographed by moving the camera \(\pm\)50 mm in the \(x\)-axis and 50 mm in the \(z\)-axis.  
The spray shape maintained a shape close to that of a curtain because the nozzle had a flat exit. In addition, the commercial nozzle used in this study was a fan-shaped nozzle having a curtain-type spraying distribution. Therefore, 2D measurement was sufficient to compare the results in terms of uniformity, because the spraying thickness is small enough to involve the depth of field of imaging; however, the spraying distribution had a thickness in depth because the thickness is small enough to involve the depth of field of imaging. Several blurred droplets, due to being out of focus, were removed by the threshold correction during image processing; however, almost particles were involved in the depth of field of imaging.  
### Measurement of Spraying Performance  
Figure 5 shows the image-processing procedure. The processed images were obtained by sequentially performing threshold correction and noise reduction on the original image, using Image J software. The particle-tracking function in Image J software was employed with the processed images for extracting spraying droplet information, including position and size. In order to check the reliability of the procedure for automatic obtaining the droplet size, we compared the value with a manual calculation for 100 droplets. The measurement accuracy in the size was about 5.3%. Figure 6 shows representative processed images for the newly proposed nozzle obtained at locations 1 and 2. For statistical analysis, spraying images were captured for 1 s, and the final droplet information was summed and averaged. From the droplet information, the spraying performances were compared by calculating various parameters, such as FWHM and droplet distributions, with respect to the radial position.  
Figure 4: Variations of flow rate and pressure with various loads.  
Figure 5: Image processing procedure. (**a**) Original image, (**b**) threshold correction, (**c**) noise reduction, and (**d**) particle tracking.  
Figure 6: _Cont._