Autonomous variable-rate UAV spot spraying using CDA nozzles and dual deposition sensing

This study presents the laboratory evaluation of a PWM-controlled spraying system for an ABZ L10 Unmanned Aerial Vehicle (UAV) equipped with controlled droplet application (CDA) nozzles, as a step toward developing an autonomous precision spot-spraying system. The consumer-grade multicopter platform, originally designed for uniform spraying, was retrofitted with a modular spraying unit incorporating CDA nozzles. A custom control architecture was developed to interface with the flight controller and synchronize spray parameters with the drone’s flight path and forward velocity. System verification and performance evaluation were central to this study. Spray deposition and spatial accuracy were assessed using two complementary measurement approaches under controlled laboratory conditions. Water-sensitive indicator papers provided qualitative and semi-quantitative information on droplet coverage and spatial distribution. In parallel, a resistance-based sensor array, conceptually analogous to rainfall sensors and developed as part of this research, delivered continuous, real-time, quantitative measurements of local spray deposition by monitoring changes in surface conductivity as droplets accumulated on the sensor surface. The combined evaluation of these measurement methods provided an objective basis for assessing nozzle performance and PWM control. From an application perspective, the primary goal of the developed system is its future integration into precision farming workflows, specifically in herbicide applications where weed presence is the primary trigger. However, for insects or diseases, additional layers of decision-making, such as economic injury thresholds, would be required to justify application. By enabling accurate identification and targeted treatment of specific zones, the system is expected to reduce unnecessary chemical usage and off-target losses, thereby lowering environmental impact while improving the efficiency and sustainability of drone-based plant protection.