In recent years, the adoption of crop spraying drones, also known as spraying UAVs, has revolutionized agricultural pest management due to their high efficiency, water-saving capabilities, and labor reduction. As a researcher focused on integrated pest management, I have observed that while hardware and control systems for these spraying UAVs have advanced significantly, the development of compatible ultra-low volume formulations remains limited. This often necessitates the use of conventional pesticides supplemented with adjuvants to enhance performance in terms of droplet deposition, evaporation resistance, and drift reduction. In this study, I aimed to evaluate the effects of various adjuvants on the efficacy of pesticides applied via a spraying UAV for controlling key rice pests and diseases, including rice planthoppers, stem borers, leaf rollers, sheath blight, and false smut. The objective was to determine whether adjuvants could significantly improve control outcomes under low-volume spraying conditions, typically 2 L per 667 m², which is common in crop spraying drone operations.
The experiment was conducted in a controlled rice field environment, where I utilized a DJI T20P spraying UAV equipped with rotary centrifugal nozzles operating at approximately 10,000 rpm, producing droplets around 100 μm in size. The UAV was flown at a height of 1.5 m above the crop canopy and a speed of 5 m/s, with applications timed in the early morning to minimize environmental losses. I selected five adjuvants—Huoniu, Huainongte, Jiexiaoli, Jijian, and Yafeng—based on their common use in agricultural practices. These were added to standard pesticide formulations, including insecticides like triflumezopyrim and fungicides such as benzovindiflupyr, at specified rates per 667 m². The experimental design followed a randomized block approach with three replications, and assessments included pest population counts, disease incidence, and severity indices post-application.
To quantify the control efficacy, I employed standard formulas for calculating percentage reduction in pest populations and disease indices. For insect pests, the control efficacy was derived using the formula: $$ \text{Control Efficacy} = \left(1 – \frac{\text{Pest count in treatment}}{\text{Pest count in control}}\right) \times 100\% $$ Similarly, for diseases, the formula was: $$ \text{Control Efficacy} = \left(1 – \frac{\text{Disease index in treatment}}{\text{Disease index in control}}\right) \times 100\% $$ where the disease index accounts for severity levels. These calculations allowed for a comparative analysis of adjuvant effects across different pests and diseases.
The results for rice planthopper control revealed that adjuvants such as Huainongte, Jiexiaoli, and Jijian enhanced the efficacy of insecticides when applied via the spraying UAV. For instance, after two applications, the control efficacy with these adjuvants reached up to 97.74%, compared to 95.07% without adjuvants. This improvement can be attributed to better droplet adhesion and penetration, which are critical in low-volume sprays from crop spraying drones. However, statistical analysis showed no significant differences between treatments, indicating that while adjuvants provide a boost, their impact may be limited under the given spray volume. The table below summarizes the control efficacy for rice planthoppers at various intervals after the second application.
| Treatment | 5 Days Post-Application (%) | 10 Days Post-Application (%) | 15 Days Post-Application (%) | 20 Days Post-Application (%) | 25 Days Post-Application (%) |
|---|---|---|---|---|---|
| Pesticide Alone | 91.67 | 97.31 | 92.25 | 95.07 | 87.25 |
| Pesticide + Huoniu | 74.94 | 96.96 | 89.46 | 91.77 | 87.51 |
| Pesticide + Huainongte | 94.13 | 98.66 | 95.60 | 97.09 | 96.77 |
| Pesticide + Jiexiaoli | 96.80 | 99.09 | 97.42 | 97.74 | 97.49 |
| Pesticide + Jijian | 93.63 | 98.30 | 97.50 | 97.51 | 95.00 |
| Pesticide + Yafeng | 90.72 | 98.22 | 95.83 | 97.07 | 89.18 |
For stem borer control, the addition of Huainongte, Jiexiaoli, and Jijian resulted in higher efficacy, with values around 73.88%, compared to 67.71% for pesticide alone. This suggests that adjuvants may improve the penetration of insecticides into the plant tissues, which is essential for targeting borers. The formula for stem borer control efficacy is: $$ \text{Control Efficacy} = \left(1 – \frac{\text{Dead heart rate in treatment}}{\text{Dead heart rate in control}}\right) \times 100\% $$ where the dead heart rate represents the percentage of damaged tillers. The table below provides a detailed comparison of the control efficacy for stem borers.
| Treatment | Infestation Rate (%) | Dead Heart Rate (%) | Control Efficacy (%) |
|---|---|---|---|
| Pesticide Alone | 8.33 | 1.57 | 67.71 |
| Pesticide + Huoniu | 10.33 | 1.80 | 61.53 |
| Pesticide + Huainongte | 6.67 | 1.22 | 73.88 |
| Pesticide + Jiexiaoli | 9.00 | 1.37 | 70.99 |
| Pesticide + Jijian | 7.33 | 1.24 | 73.81 |
| Pesticide + Yafeng | 13.00 | 1.89 | 60.10 |
In the case of leaf roller control, the adjuvants Huainongte, Jiexiaoli, and Jijian again showed improvements, with control efficacy reaching up to 89.06%. The associated leaf protection effect was also enhanced, as calculated by: $$ \text{Leaf Protection Effect} = \left(1 – \frac{\text{Rolled leaf rate in treatment}}{\text{Rolled leaf rate in control}}\right) \times 100\% $$ This indicates that adjuvants can aid in the uniform distribution of insecticides on foliage, a key factor in spraying UAV applications where coverage is critical. The data for leaf roller control are presented in the table below.
| Treatment | Larval Reduction Rate (%) | Control Efficacy (%) | Rolled Leaf Rate (%) | Leaf Protection Effect (%) |
|---|---|---|---|---|
| Pesticide Alone | -56.94 | 87.90 | 0.72 | 85.67 |
| Pesticide + Huoniu | -254.81 | 72.69 | 1.11 | 77.84 |
| Pesticide + Huainongte | -44.05 | 88.99 | 0.65 | 87.27 |
| Pesticide + Jiexiaoli | -13.33 | 88.43 | 0.64 | 87.15 |
| Pesticide + Jijian | 5.19 | 89.06 | 0.72 | 86.23 |
| Pesticide + Yafeng | -96.30 | 81.71 | 1.10 | 78.22 |
For disease control, sheath blight and false smut were evaluated. In sheath blight, adjuvants like Huoniu, Huainongte, and Yafeng provided slight improvements in control efficacy, with values up to 76.53%, compared to 73.59% for pesticide alone. The disease index was calculated using the formula: $$ \text{Disease Index} = \frac{\sum (\text{Number of plants per grade} \times \text{Grade value})}{\text{Total number of plants} \times \text{Highest grade}} \times 100 $$ This highlights the potential of adjuvants in enhancing fungicide performance in spraying UAV applications, though the effects were not statistically significant. The table for sheath blight control efficacy is shown below.
| Treatment | Incidence Rate (%) | Disease Index | Control Efficacy (%) |
|---|---|---|---|
| Pesticide Alone | 4.84 | 0.67 | 73.59 |
| Pesticide + Huoniu | 4.67 | 0.60 | 76.53 |
| Pesticide + Huainongte | 5.71 | 0.66 | 74.11 |
| Pesticide + Jiexiaoli | 6.91 | 0.84 | 66.92 |
| Pesticide + Jijian | 5.75 | 0.82 | 67.67 |
| Pesticide + Yafeng | 4.63 | 0.67 | 73.75 |
In false smut control, Huainongte, Jiexiaoli, and Jijian adjuvants led to control efficacies of up to 88.26%, compared to 85.85% for pesticide alone. The disease severity was assessed using a grading system, and the efficacy was computed as described earlier. This demonstrates that adjuvants can improve the deposition of fungicides on rice panicles, which is vital for preventing false smut in spraying UAV operations. The data are summarized in the table below.
| Treatment | Disease Incidence Rate (%) | Disease Index | Control Efficacy (%) |
|---|---|---|---|
| Pesticide Alone | 3.63 | 1.77 | 85.85 |
| Pesticide + Huoniu | 4.29 | 2.34 | 81.33 |
| Pesticide + Huainongte | 3.19 | 1.73 | 86.21 |
| Pesticide + Jiexiaoli | 3.63 | 1.76 | 85.93 |
| Pesticide + Jijian | 2.89 | 1.47 | 88.26 |
| Pesticide + Yafeng | 10.52 | 3.71 | 70.38 |
Throughout this study, I considered the role of adjuvants in modifying the physicochemical properties of spray solutions, such as surface tension and viscosity, which can influence droplet behavior in crop spraying drone applications. For example, the Weber number, which relates to droplet impact, can be expressed as: $$ We = \frac{\rho v^2 d}{\sigma} $$ where $\rho$ is density, $v$ is velocity, $d$ is droplet diameter, and $\sigma$ is surface tension. Adjuvants like Jiexiaoli, which contain silicone, may reduce $\sigma$, thereby increasing $We$ and improving droplet spreading on leaf surfaces. This theoretical framework supports the observed enhancements in control efficacy.
In discussion, I found that while adjuvants such as Huainongte, Jiexiaoli, and Jijian consistently improved pesticide performance across multiple pests and diseases, the improvements were often marginal and not statistically significant under the low-volume conditions of spraying UAVs. This could be due to the limited spray volume of 2 L per 667 m², which may not fully leverage the adjuvant benefits like enhanced coverage and retention. Future research should explore higher spray volumes or adjuvant-specific formulations tailored for crop spraying drones to achieve more pronounced effects. Additionally, environmental factors such as wind and humidity could interact with adjuvant performance, warranting further investigation.
In conclusion, my evaluation demonstrates that adjuvants can provide a modest boost to the efficacy of pesticides applied via spraying UAVs for rice pest and disease control. However, under the standard low-volume settings of crop spraying drone operations, these effects are not significant. This underscores the need for optimized application strategies that integrate adjuvants with advanced spraying UAV technologies to maximize agricultural sustainability and productivity. As the use of spraying UAVs continues to grow, such insights will be crucial for developing effective pest management protocols.
For additional resources on spraying UAV technologies, refer to this link.