In recent years, the tea industry has faced significant challenges due to pest infestations, with the tea green leafhopper (Empoasca pirisuga Matumura) being a primary concern. This pest causes wilting of tea buds and leaves, leading to reduced tea quality and economic losses. Traditional pest control methods often rely on chemical pesticides, which can result in pesticide residues and environmental pollution. As a sustainable alternative, plant-based pesticides like azadirachtin have gained attention for their eco-friendly properties. However, the application efficiency of these pesticides is critical, and the use of crop spraying drones, or spraying UAVs, has emerged as a promising solution due to their flexibility, low operational costs, and high pesticide utilization rates. Despite these advantages, spraying UAVs are susceptible to wind-induced drift, which can reduce deposition efficiency and overall control efficacy. To address this, adjuvants are commonly added to spray solutions to enhance droplet properties, minimize drift, and improve target deposition. This study investigates the impact of different adjuvants on the spray performance of crop spraying drones and the control efficacy against tea green leafhoppers, focusing on parameters such as droplet size, deposition distribution, and residual effects.
The experiment was conducted in a tea plantation, utilizing a DJI MG-1P crop spraying drone equipped with anti-drift nozzles. The spray solution included 0.3% azadirachtin emulsion as the primary pesticide, with adjuvants such as neem oil, plant oil, and an organic silicone-based adjuvant (Red Sun) evaluated for their effects. A control group using 2.5% beta-cyfluthrin emulsion was included for comparison. The spraying UAV was operated at a height of 1 meter above the tea canopy, with a flight speed of 2 m/s and a spray volume of 4 L per acre. Field assessments involved measuring droplet deposition using tracer dyes, analyzing atomization performance, and evaluating control efficacy against tea green leafhoppers over 10 and 20 days post-application. Additionally, azadirachtin residues in tea leaves were quantified to assess safety. The experimental design included multiple replications and statistical analyses to ensure reliability, with data processed using standard software for agricultural research.
The results demonstrated that adjuvants significantly influenced the spray performance of the crop spraying drone. Droplet deposition distribution across different tea plant layers (canopy, middle, and bottom) followed a consistent pattern, with the highest deposition in the canopy layer. For instance, the addition of plant oil and neem oil increased droplet coverage and deposition amount, while the organic silicone adjuvant improved droplet density. The table below summarizes the key atomization performance parameters for different treatments, highlighting the effects of adjuvants on droplet size and distribution.
| Treatment | D50 (μm) | Droplet Coverage (%) | Droplet Density (cm⁻²) | Deposition Amount (μL/cm²) |
|---|---|---|---|---|
| Azadirachtin + Neem Oil | 324.60 | 30.00 | 120.58 | 3.24 |
| Azadirachtin + Red Sun | 335.40 | 26.50 | 109.50 | 2.78 |
| Azadirachtin + Plant Oil | 349.60 | 33.60 | 114.58 | 3.83 |
| Azadirachtin Only | 321.40 | 23.75 | 106.82 | 2.08 |
| Beta-cyfluthrin | 319.80 | 21.99 | 105.58 | 1.83 |
Droplet size distribution was characterized using parameters such as D10, D50, and D90, which represent the diameters at 10%, 50%, and 90% of the cumulative droplet volume, respectively. The span of droplet distribution (S) was calculated to assess uniformity, with a lower S value indicating more consistent droplet sizes. The formula for S is given by: $$S = \frac{D_{90} – D_{10}}{D_{50}}$$ For example, the plant oil adjuvant resulted in an S value of 1.11, suggesting superior uniformity compared to other treatments. This improvement is crucial for reducing drift and enhancing deposition in crop spraying drone applications.
Deposition amount varied significantly across tea plant layers, with adjuvants increasing the overall retention. The coefficient of variation (CV) was used to evaluate distribution uniformity, calculated as: $$CV = \frac{s}{\bar{x}}$$ where s is the standard deviation and $\bar{x}$ is the mean deposition amount. Lower CV values indicate more even distribution. The table below presents the deposition amounts for different layers, emphasizing the role of adjuvants in optimizing spray performance for spraying UAVs.
| Treatment | Canopy (μg/cm²) | Middle Layer (μg/cm²) | Bottom Layer (μg/cm²) | CV |
|---|---|---|---|---|
| Azadirachtin + Neem Oil | 1.02 | 0.30 | 0.05 | 0.78 |
| Azadirachtin + Red Sun | 1.09 | 0.32 | 0.05 | 0.86 |
| Azadirachtin + Plant Oil | 1.25 | 0.41 | 0.09 | 0.76 |
| Azadirachtin Only | 0.47 | 0.17 | 0.04 | 1.00 |
| Beta-cyfluthrin | 0.28 | 0.10 | 0.01 | 1.36 |
Control efficacy against tea green leafhoppers was assessed over time, with adjuvants demonstrating a positive impact on longevity and effectiveness. After 10 days, the combination of azadirachtin with plant oil achieved a control efficacy of 62.97%, while the Red Sun adjuvant resulted in 60.38%. By day 20, these values remained high at 57.74% and 59.95%, respectively, indicating sustained performance. In contrast, the control treatment with beta-cyfluthrin showed lower efficacy, highlighting the advantage of adjuvants in enhancing the performance of crop spraying drones. The residual analysis revealed that azadirachtin levels in tea leaves were minimal, at 0.0014 mg/kg and 0.0009 mg/kg after 10 and 20 days, respectively, which is well below established safety thresholds. This underscores the potential of spraying UAVs in integrated pest management strategies for sustainable agriculture.
The discussion delves into the mechanisms by which adjuvants improve spray performance. For instance, oil-based adjuvants like plant oil and neem oil enhance droplet adhesion and reduce evaporation, leading to better canopy penetration. The organic silicone adjuvant, Red Sun, lowers surface tension, promoting spread and coverage. These effects are critical for spraying UAVs, as they operate in dynamic environmental conditions where drift can compromise efficacy. The relationship between droplet parameters and control efficacy can be modeled using regression equations, such as: $$Efficacy = a \cdot Deposition + b \cdot Coverage + c$$ where a, b, and c are coefficients derived from experimental data. This emphasizes the importance of optimizing spray characteristics for maximum impact.
In conclusion, the integration of adjuvants with crop spraying drones significantly enhances spray performance and control efficacy against tea green leafhoppers. Plant oil and Red Sun adjuvants were particularly effective in increasing droplet deposition and uniformity, resulting in improved pest management outcomes. The use of spraying UAVs, combined with adjuvants, offers a sustainable approach to tea plantation protection, reducing pesticide residues and environmental impact. Future research should focus on adjuvant formulations tailored for specific crops and drones, as well as real-time monitoring technologies to further optimize application efficiency. This study contributes to the advancement of precision agriculture, demonstrating the synergy between modern spraying UAVs and adjuvant chemistry in addressing agricultural challenges.
Further analysis of droplet dynamics involves mathematical modeling of particle distribution. For example, the Rosin-Rammler distribution can be applied to describe droplet size spectra: $$Q(d) = 1 – \exp\left[-\left(\frac{d}{d_c}\right)^n\right]$$ where Q(d) is the cumulative volume fraction, d is droplet diameter, d_c is the characteristic diameter, and n is the distribution parameter. This model helps in predicting droplet behavior under different adjuvant conditions, aiding in the design of efficient crop spraying drone systems. Additionally, the impact of adjuvants on drift potential can be quantified using drift reduction equations, such as: $$Drift Reduction = k \cdot \Delta D_{50}$$ where k is a constant and ΔD50 is the change in median droplet size due to adjuvants. Such formulations enable farmers to select optimal adjuvant-drone combinations for specific field conditions.
The economic and environmental benefits of using adjuvants with spraying UAVs are substantial. By improving deposition efficiency, adjuvants reduce the required pesticide volume, lowering costs and minimizing ecological footprint. For instance, a cost-benefit analysis can be expressed as: $$Benefit = \frac{Efficacy \times Area}{Cost}$$ where Efficacy is derived from control rates, Area is the treatment coverage, and Cost includes adjuvant and operational expenses. This highlights the practicality of integrating adjuvants into crop spraying drone protocols for scalable and sustainable pest control. Overall, this research underscores the transformative potential of spraying UAVs in modern agriculture, driven by innovations in adjuvant technology and precision application methods.
Visualization of droplet deposition patterns, such as through the provided nan, illustrates the enhanced coverage achieved with adjuvants, reinforcing the quantitative findings. This image, though not detailed here, represents typical distribution maps used in spray analysis, showing how adjuvants help achieve uniform deposition across plant surfaces when applied via crop spraying drones. Such visual aids are invaluable for educating farmers and practitioners on the benefits of adjuvant-enhanced spraying UAV operations.