A Review on the Evolution of Air-Assisted Spraying in Orchards and the Associated Leaf Motion During Spraying

Abstract

Air-assisted spraying is vital in modern orchard pest management by enhancing droplet penetration and coverage on complex canopies. However, the interaction between airflow, droplets, and flexible foliage remains unclear, limiting spray efficiency and environmental sustainability. This review summarizes recent advances in understanding leaf motion dynamics in wind and droplet fields and their impact on pesticide deposition. First, we review orchard spraying technologies, focusing on air-assisted systems and their contribution to more uniform coverage. Next, we analyze mechanisms of droplet deposition within canopies, highlighting how wind characteristics, droplet size, and canopy structure influence pesticide distribution. Special attention is given to leaf aerodynamic responses, including bending, vibration, and transient deformation induced by wind and droplet impacts. Experimental and simulation studies reveal how leaf motion affects droplet retention, spreading, and secondary splashing. The limitations of static boundary models in deposition simulations are discussed, along with the potential of fluid-structure interaction (FSI) models. Future directions include integrated leaf-droplet experiments, intelligent airflow control, and incorporating plant biomechanics into precision spraying. Understanding leaf motion in spray environments is key to enhancing orchard spraying efficiency, precision, and sustainability.

1. Introduction

Controlling pests and diseases in fruit trees is essential for modern agricultural production [1,2,3,4], and orchard spraying technologies directly influence pesticide utilization efficiency and environmental impact [5]. Traditional orchard spraying methods, often dependent on manual labor or conventional sprayers, exhibit significant droplet drift and poor deposition on inner canopies and leaf undersides, leading to pesticide waste and environmental pollution [6,7]. In recent years, precision and efficiency have become central themes in orchard plant protection [8,9]. Air-assisted spraying, which integrates airflow with droplet delivery, has been widely adopted in recent years [10]. As early as 2001, the Food and Agriculture Organization of the United Nations (FAO) endorsed air-assisted spraying for orchard use [11,12]. By employing high-speed airflow from blowers to transport droplets deep into the canopy, air-assisted spraying enhances droplet penetration and ensures uniform pesticide coverage on both sides of leaves [13]. Studies have shown that appropriate auxiliary airflow improves canopy deposition and reduces drift losses under natural wind conditions, thereby enhancing pesticide utilization efficiency [14].

However, achieving uniform and efficient pesticide deposition on fruit trees remains challenging [15]. In particular, inadequate deposition on leaf undersides has remained a persistent problem [16]. Most fruit tree pests and pathogens colonize or infect from leaf undersides, making adequate droplet coverage critical for effective pest and disease control [17]. Air-assisted spraying can induce leaf flipping and vibration through airflow disturbances, increasing the exposure of leaf undersides to pesticide droplets [18]. Leaf motion characteristics, such as swinging and twisting in a wind field, along with their dynamic response to droplet impacts, play a critical role in determining droplet deposition patterns [19].

Numerous studies have been conducted on both spray deposition in orchards and plant leaf motion. On one hand, experimental and simulation studies have analyzed droplet transport and deposition in air-assisted spraying, focusing on wind field conditions, droplet size, and canopy structure [20]. On the other hand, substantial progress has been made in biomechanics and fluid dynamics, particularly in understanding wind-induced leaf vibrations, fluttering, and droplet leaf impact dynamics [21,22]. However, integrated studies combining these domains, especially those focusing on the motion behavior of specific fruit tree leaves (such as peach trees) under realistic spraying conditions, remain limited.

This review systematically synthesizes global research on the motion characteristics of peach tree leaves under coupled wind-droplet fields. It first provides an overview of air-assisted spraying principles and equipment evolution, then analyzes current precision-spraying trends and key factors affecting effectiveness, and finally examines droplet deposition patterns within canopies together with leaf dynamic responses to airflow and droplet impacts. Finally, the review identifies current limitations and outlines future research directions to guide the development of optimized spraying strategies and next-generation equipment.

2. Development of Orchard Spraying Technologies

2.1. Trends in Precision Spraying for Orchards

With the rise of precision agriculture [23,24], orchard spraying has been evolving toward greater accuracy and automation. Precision spraying emphasizes applying pesticides based on actual crop needs delivering the right amount at the right time to balance effective pest and disease control with reduced pesticide usage and environmental impact [25]. This approach includes spatially variable spraying tailored to canopy structure and temporally optimized applications based on pest and disease dynamics [26,27].

To achieve these goals, an increasing number of advanced technologies have been integrated into orchard plant protection. For example, sensor technologies, such as light detection and ranging (LiDAR) and ultrasonic sensors are employed to detect canopy volume and density, allowing real-time adjustment of nozzle flow rates and activation patterns [28]. Global positioning system (GPS) and geographic information system (GIS) technologies enable prescription map-based spraying, with operational parameters adjusted according to field and plant level variability [29]. Machine vision and artificial intelligence are also utilized to detect pest and disease hotspots for targeted spraying [30]. Emerging algorithms and novel applications of LiDAR [31,32] and ultrasonic sensors are advancing the precision and efficiency of orchard spraying. Numerous studies have demonstrated that precise spray control can significantly reduce pesticide use in orchards without compromising pest control efficacy. Agricultural automation has made significant advances. Figure 1 presents several prototypes of precision pesticide-application technologies. New technologies, including autonomous orchard spraying robots and unmanned aerial systems, are being developed. These innovations enable more precise and efficient pesticide application.

2.2. A Comparative Study of Mechanically Assisted and Conventional Spray Application Methods

Mechanically assisted spraying refers specifically to methods that enhance spraying effectiveness through mechanical devices such as blowers or electrostatic systems. Compared with conventional spraying, which relies solely on boom pressure and nozzle dynamics, assisted spraying demonstrates clear advantages [34]. Air-assisted spraying as a representative example of assisted techniques, has largely replaced traditional manual sprayers in orchards.

On the one hand, assisted spraying reduces labor intensity and improves operational efficiency. Tractor-mounted or self-propelled sprayers offer significantly higher travel speeds and spray widths than manual operations, enabling timely coverage of large orchard areas [35]. On the other hand and more importantly, it improves droplet coverage [36]. By using forced airflow, air-assisted sprayers can deliver pesticide droplets to the inner and lower parts of the canopy, which are often poorly covered by conventional methods.

Assisted spraying achieves significantly higher deposition on leaf undersides compared to conventional methods. In vineyard trials, a novel electrostatic air-assisted sprayer showed improved droplet distribution uniformity throughout the canopy. It reduced water usage by 68% compared to a conventional multi-row sprayer [37]. Similarly, Zhu et al. reported that appropriate auxiliary airflow could reduce droplet drift by over 20% [38].

However, improper use of assisted spraying, such as excessive airflow or misaligned nozzles, can lead to pesticide waste or even increased drift [39]. Therefore, the advantages of mechanically assisted spraying can only be fully realized when coupled with properly optimized operational parameters.

It is worth noting that conventional orchard spraying often leads to over-application in the upper canopy and insufficient coverage in the inner regions. Mechanical assistance, particularly airflow, can partially mitigate this issue [10]. Assisted spraying technologies are advancing rapidly. Adjustable air-assisted flow and controllable nozzle output now outperform conventional constant-rate spraying. Environmental regulations are becoming stricter, and pesticide costs are rising. Mechanically assisted precision spraying is thus expected to become the dominant strategy for orchard plant protection.

Table 1. Performance Comparison of Orchard-Spraying Technologies.

Sprayer Type	Key Performance Metrics	Pesticide Use and Drift	Typical Operational Costs	Suitable Orchard Scale
Conventional Air-Blast Sprayer	limited inner-canopy penetration in dense foliage.	High off-target losses (often 50–80% of spray volume does not hit target).	Moderate initial cost (requires tractor); high ongoing chemical cost due to waste.	All scales.
Precision Variable-Rate Sprayer	Adjusts flow per tree section in real time; maintains uniform deposition similar to conventional	30–67% reduction in pesticide usage for same efficacy; drift greatly reduced.	High initial cost (sensor, controller retrofits).	Medium to large orchards benefit most (scale amplifies savings); gaining commercial adoption.
Electrostatic Sprayer	Produces finer, charged droplets that adhere to plant surfaces; can improve underside coverage.	Up to +10% leaf underside deposition observed in trials; drift potentially lowered by droplets sticking to foliage.	Moderate add-on cost (HV generator, electrodes); requires maintenance of charging system.	Small to medium orchards; used in high-value crops.
Tunnel Sprayer	Enclosed spray zone around trees; recycles unused spray; very uniform coverage.	Drift reduced ~90% (spray is contained); up to 30% less chemical needed due to recapture	High equipment cost; heavier and more complex to operate.	Large commercial orchards or environmentally sensitive areas. Often used in vineyards.
UAV sprayer	Aerial application with downwash aiding coverage on treetops; extremely agile targeting.	Very low drift beyond target (downward air limits off-target spread); reduced coverage inside dense canopies	Equipment cost per ha is high (many battery swaps, low volume per flight); requires skilled operation.	Small orchards, spot treatments, or hard-to-access plots.

summarizes the major orchard-sprayer technologies and compares their performance, operational costs, and appropriate deployment scales. In summary, orchard-sprayer development is trending toward precision and sustainability-leveraging technology to apply pesticides only where and when needed while minimizing human exposure and environmental drift.

2.3. Key Factors Influencing Orchard-Spraying Effectiveness

Orchard-spraying effectiveness is influenced by multiple factors, including equipment parameters, canopy characteristics, and environmental conditions:

Spray parameters. Droplet size is a critical factor. Droplets that are too small (diameter < 100 µm) are highly susceptible to drift loss [40], whereas excessively large droplets tend to roll off or rebound from leaf surfaces, resulting in waste [41]. Medium-sized droplets (200–300 µm) generally provide a good balance between canopy penetration and leaf adhesion, making them suitable for orchard applications [42]. Spray pressure and nozzle type determine the droplet size spectrum. High pressure typically generates finer droplets, increasing the risk of drift [43,44,45]. Therefore, appropriate nozzle types and spray pressures should be selected based on the target pest or disease to maintain droplet size distribution within an optimal range.

Spraying speed and distance. These are also important parameters. Excessive tractor speeds reduce droplet residence time near the canopy, lowering deposition rates. Conversely, speeds that are too low reduce operational efficiency and may cause over deposition and runoff. A study on pear trees found that tractor speed had little effect on overall droplet density. However, speed significantly affected deposition distribution. Specifically, higher speeds increased deposition on the windward side and decreased it on the leeward side. Therefore, operational speed should be adjusted based on canopy density and blower output [46].

Nozzle layout (orientation and angle). The direction and angle of nozzle placement significantly affect droplet deposition, especially on leaf undersides. Xu et al. demonstrated that optimizing nozzle spray angles to enhance air-induced leaf disturbance significantly improved deposition efficiency on leaf surfaces [11]. Specifically, the deposition rate on the undersides of mid and upper canopy leaves increased from below 30% to nearly 50%, in some cases even doubling.

Canopy and leaf characteristics. Canopy structure directly influences how easily droplets penetrate and are intercepted. In dense canopies with thick foliage, most droplets are intercepted by outer leaf layers, resulting in poor deposition within the inner canopy. Experiments have shown that even with adequate spray volume, insufficient air assistance can result in poor deposition on interior canopy leaves [47]. Therefore, to improve pesticide utilization efficiency, airflow intensity should be tailored to canopy structure and growth stage to ensure sufficient disturbance and enhance droplet penetration.

Leaf angle influences the degree to which the leaf surface is exposed to incoming spray [48]. If the abaxial (underside) surface faces away from the airflow, deposition efficiency decreases significantly. Simulation studies have shown that when the angle between the leaf and airflow exceeds 5°, the probability of abaxial surface exposure exceeds 95%, substantially improving deposition efficiency [11].

In addition, the physical properties of the leaf surface such as waxiness and pubescence affect droplet adhesion, spreading, and retention [49]. Hydrophilic surfaces promote droplet spreading and adherence, while superhydrophobic surfaces tend to cause droplet rebound or runoff [50]. For instance, dense trichomes can reduce droplet splashing and enhance retention. Peach tree leaves are typically smooth, waxy, and narrow, and these features significantly influence their motion under droplet impact as well as liquid retention behavior.

Environmental factors. Wind, temperature, and humidity also strongly affect spraying outcomes. High wind speeds can blow droplets off course before they reach the target, leading to substantial drift losses. Therefore, spraying under high wind conditions should be avoided or accompanied by drift-reduction strategies, such as adjusting nozzle orientation, lowering spray height, or using drift reducing or air-assisted nozzles [51]. Air-assisted spraying can partially compensate for light to moderate wind by generating directional airflow that stabilizes droplet trajectories and reduces turbulence induced drift.

Temperature and humidity significantly influence droplet evaporation. Under hot and dry conditions, small droplets may evaporate entirely before reaching leaf surfaces [52]. Therefore, in arid and high temperature climates, increasing droplet size or applying sprays during cooler periods such as early morning or late evening may be advisable.

Orchard topography and tree spacing also affect spraying effectiveness. In mountainous orchards, complex wind patterns and restricted machinery access may necessitate specialized equipment or manual assistance. Therefore, field spraying must consider current environmental conditions when selecting application windows and operational parameters. For orchards on uneven terrain, spray volume should be calibrated to ensure adequate deposition and minimize drift losses [53].

In summary, the effectiveness of orchard plant protection results from the combined interaction of spraying technology, fruit tree biology, and environmental conditions. Modern precision spraying, supported by multi-sensor systems and spray control technologies, can reduce operational uncertainties. However, practical success still relies on both scientific planning and field-level operational experience. These elements enable adaptation to specific field conditions.

3. Development of Air-Assisted Spraying Technology

3.1. Principles of Air-Assisted Spraying

Air-assisted spray, which uses high-speed air flow as a carrier to facilitate droplet delivery [43]. In typical systems, a blower mounted on the sprayer generates a directed, high-velocity airflow that carries atomized droplets from the nozzles to the surfaces of target crops [54]. Alternatively, the high-speed airflow can create a velocity differential with the liquid stream, atomizing it into fine droplets that are subsequently transported to the target [45].

Compared with conventional nozzles, air-assisted airflow provides additional kinetic energy and penetration force, allowing droplets to penetrate crop canopies, reach inner foliage, and promote leaf movement [55]. Thus, a key advantage of air-assisted spraying is its ability to achieve strong droplet penetration and uniform deposition on both sides of leaves, which is especially beneficial for tall crops such as fruit trees [56].

Air-assisted spraying can also partially offset the effects of environmental wind: by adjusting airflow speed and direction appropriately, it reduces drift and improves pesticide utilization [57]. However, it is important to note that excessive airflow can blow droplets beyond the canopy, resulting in waste and environmental contamination, while insufficient airflow leads to poor internal canopy deposition [58]. Therefore, air-assisted spraying requires airflow parameters to be tailored to canopy structure and field conditions, ensuring proper coordination between air volume and spray volume [59].

3.2. Evolution and Application of Air-Assisted Spraying Technology

Air-assisted spraying technology originated in orchards of developed countries in the mid-20th century and has since undergone continuous evolution and refinement [60,61]. Traditional orchard air-assisted sprayers typically utilize large centrifugal or axial fans mounted at the rear of a tractor. These fans direct airflow horizontally to both sides, carrying droplets toward adjacent trees [62,63]. Although these systems feature simple structures and high operational efficiency, they often lack the capability to adjust airflow volume or adapt precisely to varying canopy architectures [64].

To improve targeting accuracy, researchers worldwide have introduced various enhancements to air-assisted spraying technology. In countries such as the United States, tower sprayers have been widely adopted. These sprayers feature vertical, tower shaped fan outlets that conform to the canopy contour, delivering air and droplets vertically across multiple canopy layers and improving deposition uniformity [65].

In Europe, multi-stage adjustable airflow systems have also been investigated. For example, Landers et al. developed a louver based air deflector system that increased leaf deposition by approximately 30% in field trials [66]. Cross demonstrated that reducing fan speed to lower airflow volumes significantly enhanced droplet coverage within the canopy [63].

More recently, the concept of differentiated airflow distribution across canopy zones has gained increasing attention. Feng et al. developed a multi-channel air-assisted sprayer capable of delivering variable airflow to different canopy heights and densities [10]. Experiments have confirmed that differentiated airflow enhances deposition. However, simultaneous adjustment of independent channels can lead to airflow interference. This interference remains an unresolved challenge

To address this issue, Doruchowski et al. proposed the Crop Adapted Spraying System, which synchronizes fan speed and inlet aperture to minimize cross interference among multiple air outlets [67]. This system introduced the use of electric actuators to control air intake areas; however, further improvements in airflow control precision are still required.

Since the late 20th century, China has introduced and domestically developed air-assisted sprayers for orchard applications. Currently, most orchard sprayers used in China integrate the spray system with a fan unit, allowing adjustment of fan angle and airflow volume based on tree architecture to reduce pesticide use, improve coverage uniformity, and minimize drift. For example, in southern China, lateral air-assisted sprayers have been developed for citrus orchards, whereas in northern regions, recycling type air-assisted sprayers have been designed for densely planted orchards [37].

Overall, air-assisted spraying technology has evolved from a one size fits all, high airflow approach into a precision oriented system featuring adjustable airflow rates and controllable directions. Its application has extended beyond traditional tractor-mounted sprayers to include self-propelled and remote controlled tracked platforms. For example, researchers have developed an orchard-spraying robot based on a tracked autonomous platform that integrates remote control and allows synchronized adjustment of travel speed and fan airflow, enabling on demand airflow delivery according to canopy structure and growth stage [10]. These advancements enable air-assisted sprayers to better accommodate the diverse requirements of different fruit species, planting systems, and developmental stages.

3.3. Existing Air-Assisted Spraying Equipment and Their Characteristics

Currently, air-assisted spraying equipment used in orchards can be broadly categorized into the following types:

Mounted or Trailed Air-assisted Sprayers. These sprayers are tractor powered, equipped with large axial or centrifugal fans and spray assemblies mounted at the rear [68]. Their main advantages include high airflow capacity and extended spray range, enabling simultaneous coverage of multiple trees. However, they lack precision in low target or low airflow applications and are prone to drift. Typical examples include conventional orchard air-assisted sprayers and tower type sprayers.

Self-Propelled Air-assisted Sprayers. These machines are mounted on self-propelled platforms and typically incorporate multi-stage airflow systems with intelligent control functions. They can adjust spray and airflow rates based on driving speed and canopy conditions, enabling variable-rate application. For example, advanced intelligent air-assisted sprayers equipped with LiDAR can monitor canopy volume in real time and dynamically adjust nozzle activation and fan speed to minimize overspray [69].

Multi-Channel Precision Sprayers. These systems are equipped with multiple independently controlled air delivery channels or outlets, enabling zoned spraying across different canopy heights and inner or outer regions. Although structurally more complex, they offer improved droplet penetration and deposition in dense canopies. However, managing airflow interference between channels remains a key technical challenge [70].

Electrostatic Air-Assisted Sprayers. These systems combine air-assisted spraying with electrostatic charging devices that impart electric charge to droplets, enhancing their adhesion to crop surfaces. Aerodynamic forces cause droplets to reach the fruit tree canopy quickly. This rapid delivery reduces droplet charge loss. Studies have shown that electrostatic spraying can significantly improve leaf surface deposition, with some trials reporting up to an 11% increase in abaxial coverage [71]. However, under open field conditions, charged droplets may experience challenges such as charge dissipation and distortion of the spatial electric field during penetration of dense canopies.

Pneumatic Atomization Devices. These systems use vortex blowers, centrifugal fans, or air compressors to supply air, which is delivered to the nozzles via pipelines as compressed or accelerated airflow. The velocity difference between the liquid and the high-speed airflow within the nozzle causes the liquid to atomize into fine droplets. Simultaneously, the droplets are entrained by the airflow and directed toward the target [72]. This method generates finer droplets than conventional air-assisted spraying but requires higher blower performance, such as increased airflow capacity or higher pressure. Pneumatic atomization is applicable not only in orchards but also in other domains, such as soilless cultivation.

Unmanned aerial vehicle (UAV) Based Spraying. Although not a conventional air-assisted method, agricultural drones utilize rotor induced airflow to direct droplets downward, which can be regarded as a form of airflow assistance [73]. Currently, agricultural drones are widely used in low growing crops, while their application in orchards remains at an exploratory stage. Their key advantages include high mobility and minimal dependence on terrain. However, limitations such as low payload capacity and inadequate downwash airflow penetration in dense canopies hinder their overall effectiveness. To address these limitations, researchers have investigated mounting air-assisted modules on drones or implementing coordinated multi-drone operations to enhance spraying performance in fruit trees.

To compare these air-assisted systems,

Table 2. Comparison of Advanced Air-Assisted Sprayer Systems.

System Type	Droplet Size (VMD) and Distribution	Canopy Penetration and Deposition	Spray Drift Tendency
Tower Sprayer	Medium to coarse droplets (200–400 µm VMD) reduce drift while the directed air carries them through canopy. Some use twin-fluid nozzles to maintain droplet size at various heights.	These sprayers achieve excellent vertical penetration. They provide uniform top-to-bottom deposition, with inner-canopy coverage improved by approximately 30% compared to a standard fan.	Fan speed reduced drift significantly lower than conventional spraying, comparable to no-air drift at only 0–30% power consumption.
Multi-Channel Sprayer	Medium droplets (180–300 µm), potentially adjustable by zone. Could employ different nozzle types per channel. Generally avoids extremely fine droplets to limit drift in each channel’s stream.	Multiple airflow channels direct spray into specific canopy layers upper, middle, and lower. This design achieves very high coverage even in dense sections. Laboratory simulations indicate that optimizing each channel can increase coverage by 15–20% in dense canopy regions. Field prototypes significantly improved deposition uniformity.	Overall drift is lower than in single-fan systems because unnecessary airflow is eliminated. Preliminary estimates suggest overall drift reductions of approximately 20–50%, pending comprehensive field validation.
Electrostatic Air-Assist Sprayer	Fine to medium droplets (50–200 µm) with electrostatic charge (usually 3–6 kV charge applied). Droplet spectrum skews smaller to maximize charge effects, but airflow helps ensure reach.	Electrostatic attraction improves coverage on concealed and abaxial (underside) leaf surfaces. In laboratory and field tests, abaxial coverage increased by up to 11%. In dense canopies, charged droplets do not markedly outperform uncharged droplets in reaching the deep interior.	Drift-reduction effect is inconsistent: when droplets are extremely fine, some may still escape before reaching a target surface. Overall, only modest drift mitigation (10–30%) has been observed under typical conditions.

summarizes key performance indicators for tower, multi-channel, and electrostatic sprayers. The metrics include typical droplet size range, canopy penetration achieved, drift impact, and relative cost/complexity.

In summary, modern air-assisted spraying equipment is evolving toward higher levels of precision, intelligence, and multifunctionality. Figure 2 lists three types of orchard pesticide sprayers. The development of air-assisted technology has shifted toward customizable airflow and droplet output-evolving from uniform blasts to spatially and electrostatically tuned delivery. Modern air-assisted sprayers can generate a spectrum of droplet sizes-typically targeting medium droplets of 200 µm for optimal penetration and adhesion and can modulate both airflow speed and direction. Through optimized fan designs and refined control strategies, these systems continue to advance in droplet penetration, deposition uniformity, and environmental sustainability. In practice, operators must select the appropriate sprayer type based on orchard scale and tree characteristics. They must also carefully adjust the airflow speed and direction. This strategy maximizes the benefits of air-assisted spraying and ensures effective pest and disease control.

4. Droplet Deposition in Air-Assisted Spraying

4.1. Mechanisms of Droplet Deposition Within Fruit Tree Canopies

Currently, water-sensitive paper (WSP) is commonly used to measure droplet deposition at specific points within the canopy. However, WSP differs from actual leaf surfaces. It also records droplets that slide off leaves. Therefore, the amount of canopy coverage measured by WSP can only be used as a reference.

Once droplets are generated by the nozzle, their motion and eventual deposition on leaf surfaces are governed by multiple forces, primarily aerodynamic drag and gravity [76]. The primary deposition mechanism is inertial impaction, in which droplets particularly larger or faster-moving ones collide with and adhere to leaf surfaces due to their momentum or air-assisted acceleration [77]. After reaching the leaf surface, droplet behavior such as wetting and spreading, adhesion, rebound, or runoff determines the final effective deposition. On hydrophilic surfaces, droplets tend to spread and adhere, whereas on hydrophobic surfaces, they may rebound or roll off, resulting in loss [78].

In air-assisted spraying, the presence of a strong, controlled airflow further complicates the deposition process. The airflow accelerates droplets and enhances inertial impaction [79], but it also deforms leaves through aerodynamic pressure, dynamically altering the interception geometry [80]. Therefore, droplet coverage on leaf surfaces results from a triadic interaction among droplets, airflow, and canopy structure. Understanding this mechanism requires experimental observations and numerical simulations. These must cover multiple scales, from microscopic processes, such as single droplet dynamics and surface wetting, to macroscopic phenomena, such as bulk droplet transport and canopy penetration.

4.2. Influence of Wind Fields on Droplet Deposition

In air-assisted spraying, the wind field is primarily generated by a blower mounted on the sprayer. Wind field characteristics including velocity distribution, turbulence intensity, and directional angle play a critical role in shaping droplet coverage patterns.

On the one hand, sufficiently strong airflow can transport droplets deep into the canopy, enhancing deposition on inner foliage. On the other hand, excessively strong airflow may cause droplet drift, leaf fluttering, or even blow-through and loss beyond the canopy [81]. Experimental studies have shown that, within a certain range, increasing wind speed is positively correlated with droplet deposition. However, beyond a critical threshold, deposition may plateau or even decline [82]. This reduction is attributed to excessive leaf bending or increased oscillation frequency under high winds, which decreases the exposure time and effective area of the abaxial surface.

Moderate airflow can induce periodic leaf oscillation, intermittently exposing the abaxial surface to droplets and thereby enhancing deposition [83]. For example, a laboratory study with varying blower speeds found that airflow at 5–7 m/s could partially invert leaves without fully flipping them into a leeward position, resulting in maximal cumulative deposition on the abaxial surface [19]. Field trials by Xu et al. further confirmed that airflow significantly influences the vertical distribution of droplets within the canopy. Adjusting airflow direction increased abaxial deposition efficiency on upper and middle canopy leaves from below 30% to nearly 50% [11].

Therefore, optimizing both wind speed and direction is critical for effective air-assisted spraying. Wind speed should be selected based on canopy density and structure. It must be strong enough to disturb leaves without dislodging them. It should also avoid causing excessive droplet loss beyond the canopy. The wind direction should be angled slightly upward upon entering the canopy, helping to lift droplets and promote partial leaf inversion, thereby exposing the abaxial surface to the spray source [84].

The turbulence characteristics of the wind field also influence droplet dispersion within the canopy. Moderate turbulence promotes lateral droplet dispersion through leaf gaps, allowing more leaf surfaces to be reached. However, excessive turbulence may generate eddies that cause droplets to bypass leaves without contact, thereby reducing deposition [85].

Some models treat the fruit tree canopy as a porous medium to simulate its effects on airflow velocity and spray flux, enabling the prediction of droplet deposition rates [86]. Simulations show that denser canopies lead to greater canopy deposition and reduced ground-level fallout. However, when wind speed exceeds a critical threshold, the risk of spray drift increases significantly [87].

In practice, turbulence intensity is difficult to control directly under field conditions. Instead, it can be indirectly influenced by the selection of fan types and air-guiding structures. For example, deflectors or porous outlets have been used to introduce fine-scale turbulence before airflow enters the canopy, enhancing multidirectional leaf exposure to the air droplet mixture and increasing deposition efficiency [88].

Additionally, precise control of airflow direction is gaining increasing attention. By adjusting the incident angle of airflow entering the canopy, the spray can be more precisely directed to specific crown regions [75]. In summary, droplet transport within a wind field follows a complex trajectory shaped by both guided airflow and turbulent dispersion, rather than a simple linear path. Optimizing wind field parameters through field trials or computational fluid dynamics (CFD) simulations can significantly enhance the uniformity and adequacy of droplet deposition.

4.3. Effects of Droplet Size and Properties

Droplet size and velocity play a critical role in determining deposition behavior on leaf surfaces. In air-assisted spraying, droplets of varying diameters exhibit different aerodynamic responses to the wind field. Small droplets are easily transported over long distances by airflow but are highly susceptible to drift loss. Large droplets possess greater inertia and tend to impact the outer canopy directly, but may bounce off leaf surfaces or slide downward due to gravity.

Medium-sized droplets generally provide the best balance for orchard-spraying applications. However, even within the medium range, deposition patterns vary depending on nozzle type, operational parameters, and canopy architecture [89]. To enhance abaxial leaf deposition, slightly smaller droplets may be advantageous, although this comes with an increased risk of drift. A study on citrus leaves showed that droplets with a diameter of approximately 200 µm yielded the highest deposition efficiency, whereas both larger and smaller droplets were less effective [90].

In addition, initial droplet velocity governed by nozzle spray speed [91] and electrostatic charging [92] also influences droplet behavior. High-velocity droplets may splash upon impact, producing secondary droplets or rebounding from the leaf surface. In contrast, charged droplets experience electrostatic attraction as they approach leaf surfaces, which reduces rebound and enhances adhesion.

Therefore, spray deposition performance can be improved by selecting appropriate nozzles (swirl atomizing nozzles that produce medium-sized, low-velocity droplets) and incorporating adjuvants such as stickers or surfactants to modify droplet wetting behavior and enhance retention.

4.4. Influence of Canopy Structure on Droplet Deposition

Canopy structure defined by parameters such as leaf area index (LAI), leaf clustering, canopy coverage, and canopy openness [93] significantly influences both droplet cloud penetration and deposition on leaf surfaces. In denser canopies, a higher proportion of droplets is intercepted by outer leaves, reducing the amount reaching inner foliage. For dense canopies, improving deposition requires disturbing the canopy to create spray channels an effect achieved through air-assisted spraying. High-speed airflow displaces and deforms leaves, temporarily altering local leaf density and creating transient gaps that enable droplet penetration [94].

In addition to leaf density, canopy shape also affects droplet deposition and penetration [95]. Open canopies (vase-shaped or open center) allow greater air and light penetration, facilitating spray access to internal foliage. In contrast, compact canopies (spindle-shaped) exhibit overlapping inner foliage that impedes spray penetration, necessitating stronger airflow or multi-angle spraying to ensure sufficient internal coverage.

To quantitatively assess how canopy structure influences deposition, researchers commonly employ a combination of spray tracer experiments and numerical simulations. Field-based tracer studies typically involve placing targets (filter paper or water-sensitive paper) at various canopy positions to evaluate deposition patterns and compare different canopy architectures or pruning levels. In parallel, idealized canopy models such as homogeneous porous media or geometry-based reconstructions using LiDAR-derived point clouds were used in computational fluid dynamics(CFD) simulations to examine how parameters like LAI and porosity affect deposition efficiency.

For instance, Duga et al. (2015) [96] utilized 3D scanning to model fruit tree canopies and simulate airflow dynamics. Their results indicated that alignment between spray airflow direction and canopy geometry strongly influences vertical deposition across canopy layers [96]. These findings provide a theoretical basis for understanding the interaction between canopy structure and the gas-liquid two-phase flow in air-assisted spraying.

In practical applications, spray strategies should be adapted based on canopy characteristics. For dense canopies, bilateral spraying (from both sides) can improve penetration, whereas for sparse canopies, reducing airflow intensity helps minimize droplet drift.

5. Leaf Motion in Airflow Environments

5.1. Aerodynamic Characteristics of Wind-Induced Leaf Motion

When exposed to wind, leaves exhibit dynamic motion arising from both the movement of the supporting branch and the relative motion of the leaf lamina. Leaves are often modeled as cantilever beams in studies analyzing their motion characteristics under wind flow [97]. As air flows over a leaf, it exerts both drag and lift forces, causing the leaf to deflect, oscillate, or flutter [98]. Leaf motion in airflow takes various forms, primarily including static deflection, small-amplitude oscillation, and periodic vibration [99,100].

At low wind speeds, leaves typically exhibit small, stable deflections, and gentle swaying, often modeled as low-frequency damped oscillations. As wind speed increases, leaves may enter vibrational modes characterized by regular, periodic motion. These vibrations can be decomposed into two components: bending of the leaf lamina and torsion around the petiole [101].

Studies have shown that the two degrees of freedom bending and torsion can be approximated as independent harmonic oscillators, with natural frequencies determined by petiole flexibility, mass distribution, and leaf shape. In broad leaves with long petioles, torsional modes tend to dominate, whereas in narrow or sessile leaves, bending modes are more pronounced [102]. Wind tunnel experiments revealed that the natural torsional frequency of a single cherry leaf is approximately 6 Hz, with a petiole damping ratio of around 0.034 [83]. In general, larger leaves with more flexible petioles exhibit lower natural frequencies.

In addition to vibration, stable deformation under wind is a critical aspect of leaf aerodynamic behavior. When subjected to sustained wind, leaves undergo reconfiguration altering their posture to reduce frontal area and minimize aerodynamic forces. This is regarded as a self-protection mechanism employed by plants under high wind conditions. The Vogel exponent is often used to describe the nonlinear relationship between leaf drag and wind speed [83]. Typically, flexible leaves curl with the wind or align their edges with the flow at higher wind speeds, thereby significantly reducing drag. As wind speed increases further, the leaf may collapse onto the branch or undergo complex flapping deformations, yet the associated increase in drag becomes sublinear.

Experimental observations of London plane tree leaves under gradually increasing wind speeds revealed a progressive sequence: initial low-frequency swaying, followed by reconfiguration (frontal area reduction), and eventually the onset of pronounced torsional flutter. As shown in Figure 3, the sequence from oscillation to reconfiguration to flutter corresponds to distinct wind speed regimes and mechanical response stages of the leaf [103].

5.2. Leaf Vibration Characteristics and the Influence of Wind Speed

Extensive research has investigated the vibration characteristics of real leaves under wind exposure, consistently revealing their distinct multi-scale nature. The vibration frequency of individual leaves is generally higher than that of the supporting branch. For example, in a previously mentioned study on young cherry trees, the branch exhibited a swaying frequency of approximately 1 Hz, whereas local leaf vibrations occurred near 8 Hz. At low wind speeds (2 m/s), leaves primarily exhibit intrinsic high frequency, small-amplitude vibration modes, while branch movement remains minimal. As wind speed increases to moderate levels (5 m/s), branches begin to sway with larger, low-frequency motions, which become superimposed on the high-frequency vibrations of individual leaves. At higher wind speeds (approaching the flutter threshold), leaves and branches may exhibit synchronized, large-amplitude motion. However, due to aerodynamic reconfiguration, the effective wind-facing area of the leaf decreases, thereby preventing dynamic instability or flutter onset [83]. This indicates that wind speed selectively influences the vibration modes of the leaf branch system [105]. At low wind speeds, independent leaf vibrations dominate, characterized by small-scale, high-frequency motion. At higher wind speeds, branch dynamics prevail, resulting in large-scale, low-frequency vibrations. For an individual leaf, both vibration amplitude and frequency vary with wind speed. In general, amplitude increases with wind speed, while frequency may gradually decrease until the flutter point, where a sudden drop in frequency is observed.

Leaves with different shapes and sizes exhibit distinct wind-induced vibration responses. For instance, narrow, elongated peach leaves tend to flap vertically along their plane, whereas broad, rounded leaves such as lotus leaves are more susceptible to torsional twisting and surface wrinkling. Therefore, when analyzing the motion behavior of peach tree leaves, it is important to consider their distinctive long elliptical geometry: a high aspect ratio and flexible petiole result in low torsional stiffness and a marked tendency for edge curling. As shown in Figure 4, the twisting angle and direction of peach leaves vary under different wind speeds. Zhang et al. conducted simulations demonstrating that changes in airflow conditions lead to leaf reorientation and deformation, thereby altering its exposure to incoming wind [104]. Jiang et al. further observed that a leaf’s dynamic response to airflow speed variations directly influences spray deposition performance [106].

5.3. Relevant Fluid Dynamics Theories and Simulations

Leaf motion in airflow is a classic fluid-structure interaction (FSI) problem, involving the coupling of aerodynamic forces with the solid mechanics of plant tissues [108]. Simplified models often represent a leaf as a single degree of freedom (SDOF) damped oscillator for instance, focusing solely on torsion about the petiole, modeled using a torsional spring to capture petiole flexibility. These models can be used to derive the steady-state deflection angle of a leaf under steady wind, as well as its vibrational response under unsteady wind conditions [109]. More advanced approaches incorporate both torsional and bending deformations by discretizing the leaf into beam elements, computing structural response using the finite element method (FEM), and coupling it with CFD to solve for aerodynamic loads. When the leaf is modeled as a mass-spring system, its effective damping comprises both structural and aerodynamic components. As wind speed increases, a negative aerodynamic damping coefficient indicates the onset of self-excited oscillation (flutter). This theoretical framework has been widely adopted to explain the emergence of large-amplitude leaf vibrations under high wind conditions.

In addition to analytical models, numerical simulations play a pivotal role in investigating wind-induced leaf motion. Coupled CFD and structural dynamics simulations enable high-resolution tracking of leaf kinematics and the surrounding flow field. For example, the aforementioned cherry tree wind tunnel experiment was numerically replicated using high-performance computing [83]. In the simulation, the entire small tree was discretized into multiple flexible elements. Results showed that at a wind speed of 8 m/s, leaf-induced drag dominated total canopy resistance. The flow disturbance frequency induced by leaf motion was approximately 8 Hz, consistent with experimentally observed leaf vibration frequencies. This confirmed that the simulation model effectively captured local leaf vibration characteristics.

Another study employed a direct numerical simulation (DNS) approach, treating the leaf surface as an FSI boundary. Using a FEM solver for structural analysis and a Lattice Boltzmann (LB) solver for flow dynamics, the simulation captured time-resolved leaf deformation under wind loads and the collective dynamics of multiple leaves. The simulation further revealed position-dependent leaf responses, illustrating spatial heterogeneity in wind-leaf interactions [80].

Although such high-fidelity simulations are computationally intensive, they offer valuable and intuitive insights into leaf behavior under airflow. Overall, the integration of theoretical and simulation-based approaches has greatly advanced our understanding of individual leaf dynamics in wind fields. These approaches have also deepened insights into clustered leaf dynamics. This progress lays a robust foundation for future studies on leaf-droplet interactions in spraying environments.

6. Leaf Motion Induced by Droplet Impact

6.1. Dynamic Effects of Droplet Impacts on Leaves

In addition to continuous airflow, the impact of individual droplets or even raindrops can also trigger transient mechanical responses in leaves. Although the average diameter of agricultural spray droplets (100–500 µm) is much smaller than that of raindrops (several millimeters), in orchard-spraying applications, droplets often travel at relatively high velocities and are concentrated in distribution, leading to non-negligible cumulative effects.

When a droplet strikes the leaf surface, it exerts an impulsive force that causes minor deformation and vibration of the leaf. Bhosale et al., using superhydrophobic Katsura leaves as a case study, employed high-speed imaging and theoretical analysis to reveal that the complex leaf motions following raindrop impacts could be decomposed into two primary modes: bending and linear torsion. These motions were modeled as damped spring oscillators, with experimentally measured frequencies and amplitudes closely matching theoretical predictions [102].

The study also found that the location of droplet impact influences energy partitioning. Impacts at the center of the leaf tend to induce more bending, while edge impacts lead to stronger torsional motion. Although the resulting leaf movement is typically only a few millimeters, it can change the leaf’s posture and thereby influence the subsequent deposition of droplets. For example, if an initial impact flips the leaf momentarily, the following droplets may deposit on the previously hidden abaxial (underside), enhancing deposition in that area.

Conversely, the elastic response of the leaf also influences the behavior of the droplet itself. Roth Nebelsick et al. observed that elastic motions across multiple scales including local deformation, flapping, torsion, bending, and petiole swinging occur at distinct time scales following droplet impact [110]. Early arriving droplets may modify the leaf’s inclination angle, thereby influencing the trajectories of subsequent droplets either directly or indirectly. Specifically, post-impact leaf oscillations may cause attached droplets to slide or detach, or may alter the angle of incidence for incoming droplets.

Moreover, large droplets often induce splashing, fragmenting into smaller satellite droplets upon impact. The elastic response of the leaf modulates the angle and extent of splashing: soft leaves absorb more impact energy through deformation, thereby reducing splashing, whereas rigid leaves rebound more strongly, generating broader splash patterns. Some leaves possess dense surface trichomes that suppress splashing by buffering impact forces and restricting liquid film spread during the initial contact phase.

In agricultural spraying, the kinetic energy of individual droplets is typically lower than that of raindrops and therefore rarely induces significant splashing. However, when droplet diameters are large or when droplets are delivered at high speeds (>400 µm or via high-velocity directed sprays), bouncing or fragmentation may still occur on the leaf surface. In summary, although droplet-induced leaf motion is typically subtle and transient, it constitutes a critical component of leaf-droplet interactions during spraying and warrants further investigation.

To assess the effects of droplet impingement on deposition, Xi et al. conducted a dedicated study using pear tree leaves [19]. In a wind tunnel setup, controlled airflow was introduced to induce leaf vibration while fluorescent tracer droplets were sprayed simultaneously to observe droplet behavior on the leaf surface. The results indicated that successful droplet deposition (as opposed to rebound) occurred only under specific conditions: droplet impact velocity needed to be below approximately 1.1 m/s, and impact acceleration had to be under 20 m/s². When either parameter exceeded these thresholds, droplets tended to rebound or fragment upon contact with the leaf surface.

Leaf vibration was found to modify the effective impact velocity. When the leaf moved away from the incoming droplet, the relative velocity decreased, increasing the likelihood of droplet deposition and spreading. Conversely, when the leaf moved toward the droplet, the relative collision became more forceful, increasing the probability of rebound.

The study further found that a free stream airflow velocity of 7 m/s was optimal for droplet coverage and spreading. At this wind speed, leaves exhibited minimal fluttering, and most droplets were able to adhere and spread across the surface. At higher wind speeds, leaf-oscillation amplitude increased, and localized reverse motion at the moment of impact sometimes prevented droplet adhesion. Further analysis revealed the relative importance of influencing factors: incoming wind speed had the greatest effect on deposition, followed by impact location on the leaf, and finally by the initial leaf inclination angle. These findings suggest that in practical spraying applications, controlling droplet size and velocity, along with inducing moderate leaf motion via airflow, can serve as effective strategies to optimize deposition performance.

Table 3. Model of Droplet Impact on Blades.

Authors	Targets	Models	Notes
Bhosale et al. [102]	Katsura leaf	Angular moment of drop = Instantaneous angular momentum of the leaf: $\begin{array}{l}{m}_d{v}_d{L}_b\cos \theta \cong {\displaystyle \frac{M{L}^2{\omega }_b{\theta }_b}{3}}\\ m_d{v}_d{L}_b\cos \theta \cong {\displaystyle \frac{M{L}^2{\omega }_b}{3}}{\mathrm{s}\mathrm{i}\mathrm{n}}^{-1}\left({\displaystyle \frac{{\delta }_b}{L}}\right)\\ {\displaystyle \frac{{\delta }_b}{L}}\cong \sin \left({\displaystyle \frac{3m_d{v}_d{L}_b\cos \theta }{M{L}^2{\omega }_b}}\right)\end{array}$	Where δb is the maximum bending deflection, md is the mass of drop, vd is the impact speed, Lb is the impact distance, θ is the initial angle of inclination of the leaf from the horizon, M is the mass of the leaf and L is the length of the leaf lamina.
Ma et al. [22]	Capsicum (Capsicum annuum L.) leaf	Displacement response equation: $Y(t)={\displaystyle \frac{1}{m{\omega }_{\mathrm{d}}}}{\int }_0^t X(t-\tau ){\mathrm{e}}^{-\zeta {\omega }_{\mathrm{n}}\tau }\sin {\omega }_{\mathrm{d}}(\tau )\mathrm{d}\tau$	Where m is the leaf blade mass, ζ is the damping ratio of the leaf blade system, X(s) is the excitation signal, ωd is the damped natural frequency of the system, ωn is the natural frequency of the leaf blade system and τ is the relative time.
Gart et al. [111]	polycarbonate cantilever beam	Vibration frequency of beam: ${\omega }_0={\beta }^2\sqrt{{\displaystyle \frac{EI}{m_{\mathrm{beam}}L+m_{\mathrm{drop}}}}}{\displaystyle \frac{1}{L^{3/2}}}$	Where ω0 is the vibration frequency, β is the prefactor, mbeam is the beam mass per unit length, L is the beam length, E is the elastic modulus, I is the cross-sectional inertia and mdrop is the mass of droplet.

summarizes several models of droplet impact on leaves. Although the models reviewed above offer robust frameworks for single-impact dynamics, natural raindrops, and agricultural spray droplets differ fundamentally in several respects.

First, size and mass: raindrops typically range from 1 to 5 mm in diameter, yielding masses of several orders of magnitude greater than those of 100–500 µm spray droplets commonly used in orchards; this disparity results in much higher per-impact kinetic energy for rain. Second, impact velocity and frequency: raindrops accelerate under gravity and strike foliage intermittently, whereas spray droplets are expelled at controlled lower speeds in rapid, high-frequency pulses. Third, cumulative versus single-event effects: high-mass, low-frequency rain impacts induce large single-shot deformations, whereas the lower-energy, repetitive nature of spray pulses tends to excite resonance and produce cumulative deflection and torsion over time.

These distinctions have clear implications for model selection and applicability. Models such as Bhosale et al. [102]’s single-impact oscillator may overestimate deformation amplitude under spray conditions, and Ma et al. [22]’s force-time characterization requires recalibration of natural frequency (ωₙ) and damping ratio (ζ) to capture high-frequency, small-amplitude oscillations. Gart et al. [111]’s beam analogy-effective for static or isolated impacts-should be extended to include pulsatile loading and the fluid-structure coupling inherent to air-assisted spraying.

6.2. Advances in Experimental and Simulation Studies

Recent years have witnessed significant progress in studying leaf motion under droplet impact and impulsive loading. High-speed imaging remains a primary experimental technique. For example, Bhosale et al. employed a high-speed camera operating at 900 frames per second to capture the full process of a single droplet impacting a Katsura leaf. As previously mentioned, the resulting leaf deformation was decomposed into simplified SDOF modes, including linear bending and torsion [102].

Ma et al. employed a custom 3D force sensor and a high-speed video system to simultaneously record the force and motion responses of a chili pepper leaf during droplet stimulation. The results indicated that the force experienced by the leaf during spraying could be categorized into three distinct phases: an initial rising phase, a fluctuating steady phase, and a plateau phase, with peak forces ranging from 0.25 to 0.52 mN/cm² [22].

In numerical simulations, studies on droplet impact on leaf surfaces have primarily focused on droplet deformation and spreading, often simplifying or neglecting the motion of the leaf itself. He et al. employed Fluent software (version 2019 R1) to model pesticide droplet impacts on soybean leaves and developed a corresponding experimental platform for model validation. Their study concluded that although soybean leaves exhibit relatively strong adhesive properties, droplet loss still occurred at high impact velocities. Moreover, the maximum spreading diameter of droplets initially increased with impact velocity, followed by a decline at higher speeds [112]. Jiang et al. [113] developed a CFD model to simulate pesticide droplet impacts on banana leaf surfaces. Their simulations, validated by experiments, demonstrated that the maximum spreading diameter of droplets was significantly influenced by both leaf structure and inclination angle. These studies highlight the critical role of the leaf’s initial state in determining droplet deposition efficiency.

In summary, leaf motion within combined wind and droplet fields constitutes a complex process that directly affects spray deposition efficiency as shown in Figure 5. Experimental and simulation studies have advanced our understanding of this interaction: airflow-induced leaf vibration and flipping can promote more uniform droplet distribution across leaf surfaces; however, excessive motion may also contribute to pesticide loss [10]. Therefore, this dynamic interaction should be strategically considered and optimized in practical spraying protocols.

7. Future Perspectives

Although numerous studies have investigated the motion of fruit tree leaves (peach trees) under wind and droplet conditions, current understanding and practical implementation remain limited in several critical aspects and warrant further advancement.

First, in experimental research, most existing studies tend to focus either on spray deposition efficiency or on the mechanical behavior of leaves, with few attempting to integrate both. Field experiments rarely measure leaf-oscillation amplitude and the corresponding spatial distribution of pesticide deposition simultaneously during spraying operations. This separation hinders the quantitative evaluation of both the beneficial and detrimental effects of leaf motion on pesticide retention. Future research should focus on developing synchronized measurement techniques. These techniques include high-speed imaging and wireless sensors to capture real-time leaf motion trajectories. Researchers should also integrate fluorescent tracers with image analysis to map instantaneous pesticide deposition. Data obtained from high-speed imaging and wireless sensors are complementary. High-speed cameras provide detailed qualitative images for a small number of leaves, whereas sensor networks deliver simultaneous quantitative measurements at multiple locations. By integrating the two data streams, one may discover that a particular quadrant of the canopy consistently receives less spray, or that a specific leaf-oscillation frequency correlates with reduced droplet retention. This would enable a direct correlation between leaf motion characteristics (frequency, amplitude, flipping angle) and deposition outcomes, thereby providing valuable insights for spraying parameter optimization.

Second, in numerical simulations and theoretical modeling, most existing spray deposition models treat leaves as static boundaries or incorporate their motion using statistical or time-averaged approaches. Such simplifications fail to capture the transient motion of leaves and their influence on individual droplet trajectories, potentially leading to under- or overestimation of deposition efficiency. To improve predictive accuracy, fluid-structure interaction models for spray deposition should be developed. Such models require coupling computational fluid dynamics with structural dynamics (finite element methods), enabling real-time computation of leaf deformation under aerodynamic forces while simultaneously tracking droplet trajectories and impacts. Future work could incorporate aeroelastic analysis and two-phase flow models from the aerospace domain to establish an integrated framework for simulating coupled leaf vibration and droplet dynamics.

Third, in terms of air-assisted spraying control technologies, although various airflow regulation systems have been developed and validated in research environments, commercial sprayers still typically operate with coarse and fixed airflow outputs. Next-generation orchard sprayers are expected to achieve more precise and adaptive airflow regulation. For instance, integrating vision or LiDAR sensors to detect canopy density and leaf distribution in real time could enable dynamic adjustments of fan speed and airflow direction, allowing tree-specific airflow delivery. This would enable sprayers not only to “see the tree to apply chemicals”, but also to “see the tree to deliver airflow”, thereby ensuring that each leaf receives optimal aerodynamic disturbance and chemical coverage.

Finally, environmental sustainability should be prioritized. Future studies should aim to minimize chemical loss and environmental impact while maintaining effective pest and disease control. A better understanding of the relationship between leaf motion and pesticide deposition may support strategies to reduce chemical input or control leaf movement in ways that mitigate pesticide runoff.

In conclusion, future research directions include, but are not limited to, conducting integrated experiments that combine leaf motion and droplet deposition, developing fluid-structure interaction-based simulation models, implementing intelligent and adaptive airflow control, and exploring novel spraying strategies. Interdisciplinary collaboration will be critical to achieving breakthroughs in this field. Progress will require joint efforts from experts in agricultural engineering and fluid mechanics, as well as contributions from specialists in plant physiology and horticultural science. Only through such collaborative approaches can the scientific level of orchard-spraying practices be comprehensively advanced.

8. Conclusions

This review synthesizes current research on leaf motion in coupled wind-droplet fields, using orchard-spraying technology as the central focus, and summarizes recent advances.

First, the development of air-assisted spraying technology has significantly improved orchard practices by enhancing operational efficiency. Airflow improves droplet penetration into the canopy and facilitates abaxial deposition; however, proper regulation of wind speed and direction is essential to minimize drift and chemical waste. Aligned with the trend toward precision spraying, modern equipment is evolving toward coordinated air-liquid control and sensor-driven adjustment, providing the technical foundation for optimizing leaf-level pesticide delivery.

Second, this review examined the mechanisms governing droplet deposition within tree canopies. Factors such as airflow patterns, droplet size, and canopy structure interact to determine pesticide distribution across both adaxial and abaxial leaf surfaces. Appropriately directed airflow can disturb foliage, open internal channels, and significantly maximize canopy coverage evenness. However, excessive or misdirected airflow may result in uneven coverage and increased chemical loss.

Subsequently, the aerodynamic behavior of leaves under airflow was discussed. As flexible structures, leaves undergo deflection, vibration, or even flutter under aerodynamic forces, and their response depends largely on leaf morphology and wind intensity. Peach leaves, characterized by their elongated and pliable structure, are prone to oscillation and flipping under moderate to high wind speeds, which can enhance spray coverage but also increase the risk of excessive vibration.

Leaf responses to droplet impacts and other external excitations were also reviewed. Transient deformations induced by droplet impacts influence droplet adhesion and secondary splashing. Wind tunnel experiments, high-speed imaging, and numerical simulations have revealed the complex mechanics underlying single droplet leaf interactions. Overall, leaf motion is closely coupled with droplet deposition: moderate leaf movement induced by airflow can enhance spray coverage, whereas excessive motion may result in undesirable droplet loss.

Despite recent progress, several key challenges remain. For example, how can synchronized optimization of airflow, droplet behavior, and leaf motion be achieved to ensure uniform deposition on both leaf surfaces while minimizing drift? How can models accurately predict leaf dynamics and deposition patterns across different tree species and phenological stages, to support customized spraying strategies? Addressing these questions will significantly advance the scientific development of orchard-spraying technology.

Essentially, an effective roadmap for improving orchard spraying integrates technology, agronomy, and plant science: it employs smarter machines equipped with sensors, controlled airflow, and adaptive nozzles, and is guided by a deeper understanding of plant interactions (leaf motion, canopy architecture), all executed through validated application protocols.

To this end, we emphasize the need to advance research on fluid-structure interaction mechanisms, develop intelligent airflow control systems, and promote interdisciplinary collaboration that integrates plant biomechanics into precision agriculture technologies. With continued scientific exploration and technological advancement, more efficient and environmentally sustainable orchard-spraying solutions are expected to emerge. Future research and practice should continue advancing toward this goal.