Development and Laboratory Evaluation of a Plant Detection-Based Nozzle Actuation System for Precision Spraying

This study addresses the inefficiencies and environmental concerns associated with conventional broadcast spraying in agriculture, where uniform chemical application leads to significant off-target losses and excessive agrochemical usage. A plant detection-based nozzle actuation system was developed to enable real-time, selective spraying based on canopy presence. The system integrates a LiDAR sensor for precise canopy detection, an ESP32 microcontroller for signal processing, and a solenoid valve-controlled nozzle for automated on/off spray regulation. Laboratory experiments were conducted using a controlled conveyor-based setup to simulate field conditions and evaluate the effects of forward speed, sensor–nozzle distance, and sensor–canopy distance on spray deposition. Spray performance was assessed using water-sensitive papers and image analysis techniques, while statistical analysis (ANOVA) and optimization using Response Surface Methodology (RSM) were performed. The results indicated that forward speed and sensor–nozzle distance significantly influenced spray coverage, whereas sensor–canopy distance had no significant effect. The optimized parameters (3.0 km h−1 speed, 35 cm sensor–nozzle distance, and 70 cm sensor–canopy distance) achieved effective canopy coverage (~52–55%) while substantially reducing off-target deposition. Compared to continuous spraying, the developed system maintained comparable target coverage while significantly minimizing chemical losses. The findings demonstrate the potential of sensor-based precision spraying for improving input efficiency and environmental sustainability.