Electrostatic spraying application is adopted in crop protection to prevent pest infestation, to improve product quality and to maximize yield. It involves a superposition of charges to pesticide spray droplets to attract substrate ions at obscured surfaces. The droplets wraparound effect reduces off-target deposition, enhances on-target spray and invariably improves spray efficiency. Electrostatic spraying system works effectively at optimum parameters in combination with charging voltages, application pressures, spraying height regimes, flow rate, travel speed, electrode material, and nozzle orientation. Many combinations of the system parameter settings have been systematically used by researchers for the electrostatic application, but there are unknown specific optimum parameters combinations for pesticide spraying. Since droplets chargeability influences the effectiveness of electrostatic spraying system, the parameters that produce ideal charge to mass ratio determine the functionality of the spraying deposition, retention and surface coverage. This article, therefore, analyses electrostatic system parameters that produce suitably charged droplets characteristics for effective impacting behavior of pesticides on substrates. Increasing applied voltages consequently maximizes charge-mass ratio to optimum and starts declining upon further increase in voltages beyond a critical point. This review further proposes the selection of an optimum electrostatic parameters combination that yields optimum droplets chargeability in pesticide application. Also, it is necessary to investigate the charge property of substrates prior to pesticide application in order to superpose the right opposite charge on spray droplets at rupture time during electrostatic spraying system. Keywords: electrostatic spraying, charged droplets, substrates, deposition, wraparound, charge-mass ratio DOI: 10.25165/j.ijabe.20191202.4673 Citation: Appah S, Wang P, Ou M X, Gong C, Jia W D. Review of electrostatic system parameters, charged droplets characteristics and substrate impact behavior from pesticides spraying. Int J Agric & Biol Eng, 2019; 12(2): 1–9.

Precision pesticide application is a key focus in orchard management, with targeted spraying serving as a core technology to optimize pesticide delivery and reduce environmental pollution. However, its accurate implementation relies on high-precision environmental sensing technologies to enable the precise identification of target objects and dynamic regulation of spraying strategies. This paper systematically reviews the application of orchard environmental sensing technologies in targeted spraying. It first focuses on key sensors used in environmental sensing, providing an in-depth analysis of their operational mechanisms and advantages in orchard environmental perception. Subsequently, this paper discusses the role of multi-source data fusion and artificial intelligence analysis techniques in improving the accuracy and stability of orchard environmental sensing, supporting crown structure modeling, pest and disease monitoring, and weed recognition. Additionally, this paper reviews the practical paths of environmental sensing-driven targeted spraying technologies, including variable spraying strategies based on canopy structure perception, precise pesticide application methods combined with intelligent pest and disease recognition, and targeted weed control technologies relying on weed and non-target area detection. Finally, this paper summarizes the challenges faced by multi-source sensing and targeted spraying technologies in light of current research progress and industry needs, and explores potential future developments in low-cost sensors, real-time data processing, intelligent decision making, and unmanned agricultural machinery.

An air-assisted electrostatic nozzle uses a combination of air-assisted atomization and electrostatic spray technology. This article optimizes the existing air-assisted electrostatic nozzles in terms of structural design to obtain a higher charge-to-mass ratio and a smaller droplet size. The optimized air-assisted electrostatic nozzle was studied experimentally, and the effects of liquid pressure, air pressure and applied voltage on the droplet size and charge-to-mass ratio were investigated. Comparing the effects of air pressure, liquid pressure and applied voltage on the charge-to-mass ratio and droplet size, the relationship curves of the droplet size and charge-to-mass ratio under each voltage were fitted using the Rayleigh charge limit theory. For a higher CMR during the spray operation, applied voltages between 2.5 kV and 3 kV, an air pressure between 0.4 bar and 0.6 bar, and a liquid pressure of less than 0.9 bar could be chosen. The optimized air-assisted electrostatic nozzles not only have small droplets but also have high charge-to-mass ratios, reducing the need for pesticide use and thus protecting human health and the environment.

The accurate detection of canopy characteristics is the basis of precise variable spraying. Canopy characteristics such as canopy density, thickness and volume are needed to vary the pesticide application rate and adjust the spray flow rate and air supply volume. Canopy thickness is an important canopy dimension for the calculation of tree canopy volume in pesticide variable spraying. With regard to the phenomenon of ultrasonic waves with multiple reflections and the further analysis of echo signals, we found that there is a proportional relationship between the canopy thickness and echo interval time. In this paper, we propose a method to calculate canopy thickness using echo signals that come from ultrasonic sensors. To investigate the application of this method, we conducted a set of lab-based experiments with a simulated canopy. The results show that we can accurately estimate canopy thickness when the detection distance, canopy density, and canopy thickness range between 0.5and 1.5 m, 1.2 and 1.4, and 0.3and 0.6 m, respectively. The relative error between the estimated value and actual value of the simulated canopy thickness is no higher than 8.8%. To compare our lab results with trees in the field, we measured canopy thickness from three naturally occurring Osmanthus trees (Osmanthus fragrans Lour). The results showed that the mean relative errors of three Osmanthus trees are 19.2%, 19.4% and 18.8%, respectively. These results can be used to improve measurements for agricultural production that includes both orchards and facilities by providing a reference point for the precise application of variable spraying.

With the ongoing advancement of global agricultural modernization, intelligent technologies have gained significant attention in agricultural production—particularly in the field of intelligent orchard sprayers, where notable progress has been achieved. Intelligent orchard sprayers, equipped with precise sensing and control systems, enable targeted spraying. This enhances the efficiency of crop health management, reduces pesticide usage, minimizes environmental pollution, and supports the development of precision agriculture. This review focuses on three core modules of intelligent sprayer technology: perception and intelligent control, spray deposition and drift control, and autonomous navigation with system integration. By addressing key areas such as sensor technologies, object detection algorithms, and real-time control strategies, this review explores current challenges and future directions for intelligent orchard sprayer technology. It also discusses existing technical bottlenecks and obstacles to large-scale adoption. Finally, this review highlights the pivotal role of intelligent orchard sprayer technology in enhancing crop management efficiency, improving environmental sustainability, and facilitating the transformation of agricultural production systems.

This paper presents the design of an intra-row obstacle avoidance shovel-type weeding machine. Theoretical analysis of intra-row weeding components guided the determination of the structures and parameters for key parts, including the signal acquisition mechanism, automatic obstacle avoidance mechanism, and weeding shovel. Furthermore, a hydraulic system was designed to support these functions. The design aims to optimize intra-row weeding operations, reduce labor costs, enhance weed control effectiveness, and prevent collisions between weeding equipment and grapevines. Through the construction of a mathematical model, the analysis determined the necessary minimum return speed of the hydraulic cylinder for the intra-row weeding shovel to avoid grapevines. We also established a reasonable range for the extension speed of the hydraulic cylinder to minimize areas missed during weeding. Further analysis showed that using the minimum return speed of the hydraulic cylinder effectively reduced missed weeding areas. A virtual prototype model of the weeding machine was created in ADAMS. Using the coverage rate of weeding operation as the evaluation index, single-factor simulation tests determined that the extension speed of the piston rod in the obstacle avoidance hydraulic cylinder and the forward speed of the weeding machine are the main influencing factors. The preset threshold of the control system, which triggered the automatic obstacle avoidance mechanism when the obstacle avoidance rod reached a specific angle (the “Angle Threshold”), was identified as a secondary influencing factor. Other factors were considered irrelevant. Hydraulic cylinder extension speed, weeding machine forward speed, and angle threshold were chosen as the influencing factors. Following the principles of a Box–Behnken experimental design, a quadratic regression combination experiment was designed using a three-factor, three-level response surface analysis method. The evaluation criterion focused on the coverage rate of weeding operation. A regression model was developed to determine the coverage rate of the weeding operation, identifying the optimal parameters as follows: obstacle avoidance hydraulic cylinder extension speed of 120 mm/s, forward speed of the weeding machine at 0.6 m/s, and an angle threshold of 18°. The optimized coverage rate of the weeding operation achieved 86.1%. This study serves as a reference for further optimization of intra-row weeding machines in vineyards and for other crops.