Droplet Density Optimization of a Drone-Based Air-Assisted Electrostatic Sprayer Using Hybrid Artificial Neural Networks and Ant Colony Optimization Under Laboratory Conditions

Drone-based chemical spraying in agriculture faces challenges related to operator health hazards, spray deposition, application efficiency, and environmental safety. Electrostatic charging system is a novel technology that minimizes off-target spraying losses and increases droplet deposition on the plant canopy. In electrostatic spraying, chemical consumption and application rates are reduced due to the uniform distribution and enhanced deposition of charged droplets on plant surfaces, thereby improving spraying efficacy. In this study, a drone-based air-assisted electrostatic sprayer was developed to investigate the effect of operational parameters that include forward speed, discharge rate, applied voltage (charged condition) and propeller speed on the droplet density (drops/cm2) in a cotton crop under laboratory conditions. The charge-to-mass ratio (CMR) of the developed air-assisted electrostatic nozzle was found in the range between 1.8–2.5 mC/kg. An Artificial Neural Network–Ant Colony Optimization (ANN–ACO) method was used to optimize the operational parameters for obtaining the highest charged droplet density on the plant canopy surfaces. Results showed that the charged droplet density was significantly affected by discharge rate (DR) and applied voltage (AV) followed by forward speed (FS) and was slightly influenced by the propeller speed (PS). Optimal performance was achieved at FS = 2.58 km/h, PS = 1204 rpm, DR = 558.45 mL/min and AV = 6.18 kV under the charged conditions. At these optimized parameters, an average charged droplet density of 185.83 ± 4.25 drops/cm2 (mean ± SE) was achieved. For the uncharged conditions, the optimal performance was achieved at FS = 2.80 km/h, PS = 1065 rpm and DR = 556.75 mL/min, corresponding to an average droplet density of 108.49 ± 2.15 drops/cm2. The integration of the ANN–ACO optimization algorithm with the drone-based air-assisted electrostatic spraying system can enhance precision chemical application on the cotton, improving efficiency and sustainability.