Measurements of reference ISO nozzles by high-speed imaging

Abstract

Agricultural spray characteristics determine the efficiency of a pesticide application because size and velocity affect droplet trajectory and impact behavior.At present, the relevance of different characterization techniques remains controversial since discrepancies may be significant between measurements performed in different laboratories.A digital image acquisition technique and analysis algorithm is proposed for droplet size and velocimetry measurements as an alternative to well-established techniques such as the Phase Doppler Particle Analyzer (PDPA) or laser diffraction spectrometry (LDS). The algorithm requires double exposed shadow images acquired in a back-lighted arrangement with a Particle Image Velocimetry (PIV) camera and a pulsed light emitting diode (LED). Spatial illumination heterogeneities are corrected by subtracting from each image a mean background acquired on several images without any particle. The algorithm accuracy is ensured by the rejection of out-of-focus particles using a focus parameter depending on gradient intensity at the particle edges. Thresholds for focus particle selection were determined by studying the evolution of the focus parameter and the error on particle size measurements from images containing droplets with * Corresponding author

Spray droplet impaction outcomes for different plant species and spray formulations

“…The diameter of this circle of confidence is a function of the maximum expected displacement of the droplet within the time between two successive images. A relationship between droplet velocity and diameter (De Cock et al, 2016), after traveling a distance equal to the nozzle height above the target surface, is used here to define the diameter of the confidence circle. The diameter of the potential droplet on image n + 1 ranges between 0.85 and 1.25 times the diameter of the droplet on image n to account for any droplet deformation and oscillation.…”

“…The algorithm used to locate individual droplets on each image and accurately measure their diameter has been described in De Cock et al (2016) and is summarised in Section 2.2.1 below. The droplet tracking algorithm (Section 2.2.2) used in this study has been adapted from Castanet et al (2013) for a single moving agricultural spray nozzle to compute the velocity of each impacting droplets.…”

“…For each droplet, an in-focus parameter was then computed based on the fact that gradient intensity at droplet boundaries increases with particle degree of focus. Thresholds for in-focus droplet selection were determined by studying the evolution of the in-focus parameter and the error on particle size measurement from images containing uniform droplets with a known size at various distance of the object plane (De Cock et al, 2016). The threshold was chosen to permit an error less than 5% of the droplet diameter.…”

Image analysis of shatter and pinning events on hard‐to‐wet leaf surfaces by drops containing surfactant

“…The target surface and its limit was identified using a composite background image created from the first images of the set. 30 Otsu's method 31 was used on the background image to create a binary image by which the surface and its edge could be identified using the Sobel filter. The upper third of the image was used as a control zone to check the background quality.…”

Experimental investigation of a round jet impacting a disk engraved with radial grooves

“…The images acquisition frequency was 5000 Hz. Images were analyzed with a Particle Tracking Velocimetry Sizing (PTVS) algorithm developed in Matlab and provided droplet size and velocity components perpendicular to optic axis [24]. The statistics of the droplet measurements were established for a number of drops superior to 10 000.…”