In recent years, the agricultural sector has witnessed significant advancements, with the low-altitude economy emerging as a key focus area supported by national policies. Crop spraying drones, also known as spraying UAVs, have become instrumental in modern plant protection due to their efficiency and precision. While these drones offer substantial advantages over traditional ground-based methods, such as reduced labor intensity and improved coverage, they face challenges related to working principles and system improvements. This study aims to analyze the key parameters affecting the performance of crop spraying drones, providing a theoretical foundation for optimizing their operation. We conducted field experiments to evaluate factors like flight speed, flight height, operational spray width, and application flow rate, using metrics such as droplet distribution uniformity, coverage rate, and deposition amount. By examining these elements, we seek to enhance the effectiveness of crop spraying drones in real-world scenarios, contributing to sustainable agricultural practices.
The adoption of crop spraying drones has revolutionized plant protection by addressing limitations of conventional methods. Traditional approaches, including manual and ground mechanical spraying, often suffer from inefficiencies, such as low coverage in complex terrains and excessive pesticide use, leading to environmental concerns. In contrast, crop spraying drones leverage advanced technologies to deliver precise applications, reducing waste and minimizing human exposure to chemicals. However, the performance of these spraying UAVs depends on various operational parameters, which must be optimized to achieve consistent results. This paper delves into the theoretical foundations and experimental analyses of these parameters, emphasizing the role of droplet dynamics and system configurations. Through a detailed investigation, we aim to provide actionable insights for improving the design and deployment of crop spraying drones in diverse agricultural settings.
To understand the behavior of crop spraying drones, it is essential to grasp their working principles and system components. A typical crop spraying drone consists of a flight system, a spraying system, and auxiliary systems like navigation and communication. The flight system relies on aerodynamics, with motors and propellers generating lift for stable operation. The spraying system includes a tank for pesticide storage, nozzles for atomization, a pump for liquid delivery, and control units that regulate flow and pressure. Advanced spraying UAVs incorporate sensors, such as flow and pressure sensors, to monitor and adjust parameters in real-time, ensuring uniform spray distribution. Additionally, navigation systems using GPS or BeiDou enable precise route planning, while communication systems facilitate data exchange between the drone and ground operators. Monitoring systems with cameras and sensors further enhance accuracy by assessing crop health and pest conditions, allowing for targeted applications.
Spraying technology in crop spraying drones is based on atomization principles, which convert liquid pesticides into fine droplets for even coverage. Common atomization methods include pressure atomization, centrifugal atomization, and air-assisted atomization. Pressure atomization uses high-pressure pumps to force liquid through small orifices, breaking it into droplets due to air resistance. Centrifugal atomization employs high-speed rotating discs or nozzles to create thin films that disintegrate into droplets under centrifugal force. Air-assisted atomization utilizes high-velocity air streams to disperse the liquid. The formation of droplets is influenced by factors like liquid surface tension, viscosity, and nozzle design. For instance, lower surface tension promotes smaller droplets, while higher viscosity requires more energy for atomization. Nozzle geometry also plays a role;扇形 nozzles are ideal for broad coverage, whereas conical nozzles suit concentrated sprays. Key parameters such as flight speed and height directly impact droplet behavior; higher speeds reduce droplet residence time, increasing drift, while greater heights amplify air resistance, leading to uneven deposition.
In our experimental design, we employed a multi-factor completely randomized approach to investigate the effects of various parameters on spray performance. The materials and equipment included a DJI T30 crop spraying drone, which has a maximum takeoff weight of 30 kg and a payload capacity of 15 L. This spraying UAV allows adjustable flight speeds ranging from 3 to 10 m/s and flight heights from 1 to 10 m. We selected two types of nozzles for comparison: a centrifugal nozzle (model XR11002) and a pressure nozzle (model Tee Jet 8002VS). The centrifugal nozzle offers flexibility in droplet size control through rotational speed adjustments, while the pressure nozzle ensures uniform droplet distribution. Measurement instruments included a laser particle size analyzer for droplet size distribution, an electronic balance for precise weighing, an anemometer for wind speed monitoring, and a thermohygrometer for temperature and humidity recording. These tools enabled accurate data collection under field conditions, ensuring reliable results for analysis.
The试验方案 involved four factors: flight speed, flight height, operational spray width, and application flow rate, each set at three levels as summarized in Table 1. This resulted in 81 treatment combinations, with three replicates per treatment to enhance statistical robustness. The experimental area was divided into plots of 100 m² each, separated by 1-meter-wide buffer zones to prevent cross-contamination. A randomized block design was used, with three blocks containing all treatment combinations to control for environmental variability. A control group using traditional ground-based spraying with a self-propelled boom sprayer (10-meter boom,扇形 nozzles, 0.3 MPa pressure) was included for comparison. Prior to the试验, the crop field was uniformly managed to minimize external influences, and meteorological conditions were monitored throughout to ensure consistency.
| Factor | Level 1 | Level 2 | Level 3 |
|---|---|---|---|
| Flight Speed (m/s) | 5 | 7 | 9 |
| Flight Height (m) | 2 | 3 | 4 |
| Operational Spray Width (m) | 4 | 5 | 6 |
| Application Flow Rate (L/min) | 2 | 3 | 4 |
During the试验实施, the crop spraying drone was thoroughly inspected and calibrated to ensure optimal performance. The spraying system was checked for leaks, nozzle blockages, and pump functionality. Pesticide solutions were prepared with consistent dilution ratios and added to the tank. The drone was operated along predefined routes at specified parameters, with real-time monitoring of flight status and environmental conditions using instruments like the anemometer. Post-spraying, data collection involved measuring droplet size with the laser particle size analyzer at multiple locations and heights, assessing deposition amount with the electronic balance, and analyzing coverage through image processing of water-sensitive papers. Samples were randomly collected from each plot to determine pesticide residues on crops, ensuring comprehensive evaluation.
The评估指标 for spray effectiveness included droplet distribution uniformity, coverage rate, and deposition amount. Droplet distribution uniformity was quantified using the coefficient of variation (CV), calculated as:
$$ CV = \frac{SD}{\bar{X}} \times 100\% $$
where \( SD \) is the standard deviation of droplet density samples and \( \bar{X} \) is the mean droplet density. A CV below 20% indicates acceptable uniformity. Coverage rate (\( F_G \)) was derived from image analysis:
$$ F_G = \frac{D_i}{Z_o} \times 100\% $$
where \( D_i \) is the number of pixels covered by droplets and \( Z_o \) is the total pixels in the sample area. A coverage rate above 80% is generally required for effective pest control. Deposition amount was measured in μg/cm² using the weight method, with a target of at least 50 μg/cm² for robust disease management. These metrics provided a holistic view of spray performance, enabling detailed comparisons across treatments.
The试验结果 revealed significant impacts of each parameter on spray outcomes. For flight speed, increasing from 5 m/s to 9 m/s led to a decline in droplet distribution uniformity, with CV values rising from 15.6% to 30.5%. This was attributed to reduced droplet residence time and increased instability in air trajectories. Deposition amount decreased from 15.6 μg/cm² to 10.2 μg/cm², a 34.6% reduction, due to enhanced drift and poorer adhesion. These findings underscore the need for moderate flight speeds in crop spraying drones to maintain efficacy.
Flight height also played a critical role; as height increased from 2 m to 4 m, the average droplet size grew from 156 μm to 223 μm, owing to prolonged air exposure and droplet coalescence. Deposition amount dropped from 14.8 μg/cm² to 9.6 μg/cm² (35.1% decrease), and uniformity worsened, with CV increasing from 16.2% to 28.7%. Higher altitudes exacerbated wind effects, leading to irregular spray patterns. Thus, operating crop spraying drones at lower heights is advisable for better deposition and coverage.
Operational spray width influenced coverage density; widening the spray from 4 m to 6 m reduced the average droplet density from 35.6 droplets/cm² to 22.1 droplets/cm². Uniformity suffered as well, with CV rising from 14.8% to 26.3%, due to edge effects and diluted spray concentration. This highlights the trade-off between coverage area and spray quality in spraying UAVs, suggesting that narrower spray widths may be preferable for high-value crops.
Application flow rate had a positive correlation with deposition amount; increasing from 2 L/min to 4 L/min raised deposition from 10.2 μg/cm² to 17.8 μg/cm². This resulted from higher pesticide output per unit time, enhancing surface adhesion. However, excessive flow rates could lead to runoff and waste, emphasizing the importance of balanced settings in crop spraying drones.
To summarize the results, Table 2 provides a comparative overview of the parameter effects on the评估指标. This synthesis aids in identifying optimal configurations for spraying UAVs.
| Parameter | Effect on Uniformity (CV) | Effect on Coverage Rate | Effect on Deposition Amount |
|---|---|---|---|
| Flight Speed Increase | Increases CV (worsens) | Decreases | Decreases |
| Flight Height Increase | Increases CV (worsens) | Decreases | Decreases |
| Spray Width Increase | Increases CV (worsens) | Decreases | Decreases |
| Flow Rate Increase | Minor effect | Increases | Increases |
In conclusion, this study demonstrates that flight speed, flight height, operational spray width, and application flow rate are pivotal in determining the performance of crop spraying drones. Lower speeds and heights, along with moderate spray widths and flow rates, generally yield better uniformity, coverage, and deposition. These insights can guide the optimization of spraying UAVs for enhanced agricultural productivity and environmental sustainability. Future research could explore integrated control systems and adaptive algorithms to dynamically adjust parameters based on real-time conditions, further advancing the capabilities of crop spraying drones.
The integration of advanced technologies, such as machine learning and IoT, into crop spraying drones could revolutionize precision agriculture. For instance, real-time data from sensors could be used to adjust flight parameters automatically, optimizing spray efficiency under varying environmental conditions. Additionally, the development of more efficient nozzles and propulsion systems could reduce energy consumption and improve droplet management. As the low-altitude economy expands, continuous innovation in spraying UAVs will be essential to meet the growing demands of global food security. This study lays a foundation for such advancements by elucidating the key operational parameters and their impacts, encouraging further experimentation and collaboration in the field.
In practical applications, farmers and operators of crop spraying drones should consider these findings to maximize effectiveness. For example, in windy conditions, reducing flight height and speed can minimize drift, while in dense crops, adjusting spray width and flow rate can ensure thorough coverage. Training programs and guidelines based on this research could promote the adoption of best practices, leading to more sustainable and efficient pesticide use. Ultimately, the evolution of spraying UAVs will depend on a holistic approach that combines engineering, agronomy, and data science, driving the future of smart agriculture forward.
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