Speaker
Description
Background and objectives
Digital characterization of agricultural spraying can provide high-resolution data to support more precise and predictive management of pesticide application in agroecosystems. Pulse Width Modulation (PWM) systems are increasingly used in variable-rate spraying because they regulate flow rate while maintaining a relatively stable operating pressure. However, their effect on droplet populations still requires specific characterization, as droplet size and velocity are key variables for modelling spray behaviour, deposition potential and drift risk. This study aimed to develop an image-based workflow to assess the effect of PWM operating parameters on spray droplet populations under controlled test-bench conditions.
Methods
Laboratory tests were carried out using an integrated spray test bench equipped with a hydraulic circuit, interchangeable nozzles and a PWM valve. Different nozzle types, pressures, duty cycles and modulation frequencies were tested. Droplet populations were measured in flight using a Particle/Droplet Image Analysis system, allowing simultaneous extraction of droplet diameter and velocity. The workflow focused on generating comparable datasets for volume median diameter, droplet size distribution and droplet motion under different PWM configurations. These variables were used to evaluate the potential of image-based measurements for data-driven spray characterization.
Preliminary results
Preliminary observations indicate that PWM modulation affects spray behaviour in a way that depends on nozzle geometry and operating configuration. Nozzle type remained a major factor influencing droplet size distribution, while duty cycle and modulation settings affected spray stability and repeatability. The image-based approach allowed differences among configurations to be detected without relying only on nominal nozzle parameters or flow-rate values. These results suggest that PWM-enabled sprayers should be characterized through measured droplet datasets before being integrated into variable-rate application strategies. Such datasets may support future modelling of spray behaviour and contribute to digital decision-support tools for more efficient and environmentally safer pesticide application.