Interpreting Image Patterns for Agricultural Sprays Using Statistics and Machine Learning Techniques

Abstract

The atomization of liquid spray solutions through nozzles is a mechanism for delivering many pesticides to the target. The smallest drop sizes (<150 μm) are known as driftable fines and have a propensity for wind-induced convection. Many agricultural applications include oil-in-water formulations. The experimental metrics obtained from spray images of these formulations include the distance from the nozzle origin to the drop centroid once a drop has formed; the hole location and surface area for holes that form in the liquid sheet (all hole areas approximated as polygons); the angles formed between polygon segments (whose vertices are represented as boundary points); and the ligament dimensions that form from intersecting holes, such as the ligament aspect ratio (R/L), ligament length (L), and ligament radius (width), along with the number of drops a ligament breaks up into. These metrics were used in a principal component regression (PCR) analysis, and the results illustrated that 99% of the variability in the response variable (DT10) was addressed by 10 principal components. Angles formed by the colliding holes, hole distance from the nozzle, drop distance, hole number, ligament number, and drop number were negatively correlated to the atomization driftable fine fraction, while hole area, ligament distance, ligament area, and boundary area were positively correlated. Thus, to decrease/minimize driftable fines, one needs to increase the negatively correlated metrics.