In modern agriculture, the use of crop spraying drones has revolutionized pest and disease management by enabling precise and efficient application of pesticides. As a researcher focused on enhancing agricultural productivity, I have observed that spray parameters significantly influence the effectiveness of these spraying UAVs, particularly for delicate crops like leafy vegetables. This study aims to optimize the operational parameters of a crop spraying drone for seedling vegetables in the Yongzhou area, leveraging orthogonal experimental design to identify the best combinations of flight height, flight speed, and spray flow rate. By analyzing droplet deposition density, we seek to provide quantitative insights that improve pesticide utilization and reduce environmental impact. The integration of advanced statistical methods and field trials ensures that the findings are both practical and scientifically robust, addressing the unique challenges of high-density, short-growth-cycle crops in subtropical climates.
The rapid adoption of spraying UAVs in agriculture stems from their ability to cover large areas quickly while minimizing human exposure to chemicals. However, the efficacy of a crop spraying drone depends heavily on the interplay of multiple factors, including environmental conditions and equipment settings. In this work, we employ a systematic approach to evaluate how flight height, flight speed, and spray flow rate affect droplet distribution on seedling vegetables. Our methodology involves a comprehensive orthogonal array design, followed by rigorous data collection and analysis using droplet scan technology and statistical software. This not only highlights the optimal parameters for maximum deposition but also elucidates the underlying mechanisms through variance and range analysis. By focusing on the Yongzhou region, we address local agricultural needs while contributing to the global knowledge base on precision spraying with crop spraying drones.
To begin, let us delve into the theoretical framework governing droplet deposition from a spraying UAV. The droplet density, a key metric, can be modeled using fluid dynamics principles. For instance, the relationship between spray parameters and deposition can be expressed as a function of flight height (H), flight speed (V), and spray flow rate (Q). A simplified model for droplet density (D) might incorporate these variables as follows: $$ D = k \cdot \frac{Q}{H \cdot V} $$ where k is a constant dependent on environmental factors like wind and humidity. This equation underscores the inverse relationship between flight height/speed and deposition, emphasizing the need for balanced parameter selection. In our experiments, we build upon such models to validate empirical findings, ensuring that the results align with physical laws while accounting for real-world variability.
Our experimental setup involved a widely used crop spraying drone, specifically a multi-rotor model equipped with centrifugal nozzles and a magnetic drive pump. The drone was operated in an open vegetable field during the seedling growth stage, with conditions carefully monitored to avoid extreme weather. We designed a L9(3^3) orthogonal array to test three factors at three levels each, as summarized in Table 1. This design allows for efficient exploration of the parameter space without exhaustive testing, making it ideal for field applications of spraying UAVs. Each trial combination was replicated three times to ensure reliability, and droplet deposition was measured at upper, middle, and lower canopy layers using specialized collection cards analyzed with image processing software.
| Level | Flight Height (m) | Flight Speed (m/s) | Spray Flow Rate (L/min) |
|---|---|---|---|
| 1 | 1.6 | 3 | 1.9 |
| 2 | 2.1 | 4 | 2.3 |
| 3 | 2.6 | 5 | 2.7 |
The orthogonal array combinations are detailed in Table 2, which outlines the nine treatment groups used in the study. For each treatment, we recorded droplet density data across the plant canopy, enabling a comparative analysis of how different parameter sets influence deposition. This structured approach ensures that we can isolate the effects of individual factors while considering their interactions, a critical aspect of optimizing crop spraying drone operations. The use of an orthogonal design not only reduces experimental effort but also enhances the statistical power of our conclusions, making the findings applicable to similar agricultural contexts where spraying UAVs are deployed.
| Treatment | Flight Height (m) | Flight Speed (m/s) | Spray Flow Rate (L/min) |
|---|---|---|---|
| T1 | 1.6 | 3 | 2.7 |
| T2 | 2.1 | 4 | 2.7 |
| T3 | 2.6 | 5 | 2.7 |
| T4 | 2.1 | 3 | 2.3 |
| T5 | 2.6 | 4 | 2.3 |
| T6 | 1.6 | 5 | 2.3 |
| T7 | 2.6 | 3 | 1.9 |
| T8 | 1.6 | 4 | 1.9 |
| T9 | 2.1 | 5 | 1.9 |
During the field trials, we captured visual evidence of the crop spraying drone in action, which can be viewed here: nan. This image illustrates the operational setup, highlighting the drone’s positioning and the spray pattern over the seedling vegetables. Such visual data complement the quantitative measurements by providing context for the environmental conditions and crop structure, which are crucial for interpreting deposition results. The integration of field imagery with analytical data ensures a holistic understanding of how spraying UAVs perform under real-world scenarios, reinforcing the practical relevance of our optimization efforts.
The results of the orthogonal experiments are presented in Table 3, which displays the average droplet density values along with standard deviations for each treatment across the upper, middle, and lower canopy layers. Notably, the upper canopy consistently showed higher deposition densities, with Treatment T1 achieving the maximum value of 66.83 droplets/cm². In contrast, the lower canopy exhibited lower densities, underscoring the challenge of achieving uniform coverage with a crop spraying drone. These findings highlight the spatial variability in droplet distribution and emphasize the need for parameter tuning to enhance penetration and reduce pesticide wastage. By analyzing these data, we can derive insights into the optimal settings for spraying UAVs that balance efficacy and efficiency.
| Treatment | Upper Canopy (droplets/cm²) | Middle Canopy (droplets/cm²) | Lower Canopy (droplets/cm²) |
|---|---|---|---|
| T1 | 66.83 ± 3.80 | 40.54 ± 1.85 | 14.43 ± 0.31 |
| T2 | 56.48 ± 2.51 | 36.52 ± 0.73 | 13.54 ± 0.33 |
| T3 | 55.25 ± 0.92 | 31.93 ± 0.54 | 12.76 ± 0.56 |
| T4 | 58.20 ± 2.82 | 38.17 ± 0.68 | 13.41 ± 0.05 |
| T5 | 46.46 ± 2.74 | 35.63 ± 0.35 | 12.92 ± 0.04 |
| T6 | 40.28 ± 2.52 | 38.38 ± 0.48 | 14.10 ± 0.72 |
| T7 | 46.72 ± 2.21 | 34.21 ± 0.45 | 9.32 ± 0.27 |
| T8 | 43.14 ± 2.71 | 36.47 ± 0.22 | 13.29 ± 0.02 |
| T9 | 36.35 ± 1.32 | 32.45 ± 0.64 | 12.68 ± 0.13 |
To further dissect the influence of each factor, we performed a range analysis, as shown in Table 4. The range (R) values indicate the degree of impact, with larger R values signifying greater influence on droplet density. For the upper canopy, spray flow rate emerged as the most critical factor, followed by flight speed and flight height. This can be mathematically represented by calculating the range for each factor: $$ R = \max(k_i) – \min(k_i) $$ where \( k_i \) represents the average droplet density for each level of a factor. The results guide us toward optimal level combinations, such as A2B1C3 for the upper canopy, which corresponds to a flight height of 2.1 m, flight speed of 3 m/s, and spray flow rate of 2.7 L/min. This systematic approach ensures that recommendations for crop spraying drone operations are data-driven and tailored to specific canopy layers.
| Canopy Layer | Factor | k1 | k2 | k3 | Range (R) | Optimal Level | Primary Factors |
|---|---|---|---|---|---|---|---|
| Upper | Flight Height (A) | 50.08 | 50.34 | 49.47 | 0.87 | A2 | C, B, A |
| Flight Speed (B) | 57.25 | 48.69 | 43.96 | 13.29 | B1 | ||
| Spray Flow Rate (C) | 42.07 | 48.31 | 59.52 | 17.45 | C3 | ||
| Middle | Flight Height (A) | 38.46 | 35.71 | 33.92 | 4.54 | A1 | A, B, C |
| Flight Speed (B) | 37.64 | 36.20 | 34.25 | 3.39 | B1 | ||
| Spray Flow Rate (C) | 34.37 | 37.39 | 36.33 | 3.02 | C2 | ||
| Lower | Flight Height (A) | 13.94 | 13.21 | 11.66 | 2.28 | A1 | A, C, B |
| Flight Speed (B) | 12.38 | 13.25 | 13.18 | 0.87 | B2 | ||
| Spray Flow Rate (C) | 11.76 | 13.47 | 13.57 | 1.81 | C3 |
In addition to range analysis, we conducted variance analysis to assess the statistical significance of each factor’s effect, as detailed in Table 5. The variance analysis employs the F-test, where the F-value is calculated as the ratio of the mean square of a factor to the mean square of error: $$ F = \frac{\text{Mean Square}_{\text{factor}}}{\text{Mean Square}_{\text{error}}} $$ A higher F-value indicates a more significant impact. For the middle canopy, flight height showed a pronounced effect with an F-value of 25.92, suggesting that lower flight heights enhance droplet penetration. This aligns with the physical behavior of spraying UAVs, where downwash airflow from the drone’s rotors disrupts the canopy, facilitating droplet movement to lower layers. However, excessive lowering of flight height can lead to droplet blow-off, highlighting the need for balanced parameter selection in crop spraying drone operations.
| Canopy Layer | Variance Source | Sum of Squares | Degrees of Freedom | Mean Square | F-value | Significance Level |
|---|---|---|---|---|---|---|
| Upper | Model | 742.50 | 6 | 123.75 | 8.63 | 0.107 |
| Flight Height (A) | 1.18 | 2 | 0.59 | 0.04 | 0.960 | |
| Flight Speed (B) | 272.24 | 2 | 136.12 | 9.49 | 0.095 | |
| Spray Flow Rate (C) | 469.07 | 2 | 234.53 | 16.36 | 0.057 | |
| Upper Error | Error | 28.68 | 2 | 14.34 | – | – |
| Middle | Model | 62.76 | 6 | 10.46 | 17.28 | 0.055 |
| Flight Height (A) | 31.37 | 2 | 15.68 | 25.92 | 0.037 | |
| Flight Speed (B) | 17.33 | 2 | 8.66 | 14.32 | 0.065 | |
| Spray Flow Rate (C) | 14.04 | 2 | 7.02 | 11.60 | 0.079 | |
| Middle Error | Error | 1.21 | 2 | 0.61 | – | – |
| Lower | Model | 15.69 | 6 | 2.61 | 2.94 | 0.275 |
| Flight Height (A) | 8.08 | 2 | 4.04 | 4.55 | 0.180 | |
| Flight Speed (B) | 1.37 | 2 | 0.68 | 0.78 | 0.563 | |
| Spray Flow Rate (C) | 6.23 | 2 | 3.11 | 3.51 | 0.221 | |
| Lower Error | Error | 1.77 | 2 | 0.88 | – | – |
Building on these analyses, we derive a comprehensive model for optimizing crop spraying drone parameters. The overall droplet deposition efficiency (E) can be expressed as a weighted function of the deposition densities across canopy layers: $$ E = w_u \cdot D_u + w_m \cdot D_m + w_l \cdot D_l $$ where \( D_u \), \( D_m \), and \( D_l \) are the droplet densities in the upper, middle, and lower canopies, respectively, and \( w_u \), \( w_m \), and \( w_l \) are weights reflecting the importance of each layer for pest control. In our case, for seedling vegetables, we assign higher weights to the middle and lower layers to ensure adequate coverage where pests often reside. Using this model, we computed the optimal parameter set as flight height of 2.1 m, flight speed of 4 m/s, and spray flow rate of 2.7 L/min, which maximizes E while minimizing pesticide loss. This approach demonstrates how spraying UAVs can be fine-tuned for specific crops and regions, enhancing sustainability in agriculture.
The discussion of our findings extends to the economic and environmental implications of using crop spraying drones with optimized parameters. For instance, by adopting the recommended settings, farmers can achieve higher pesticide utilization rates, reducing the amount of chemicals needed and lowering costs. Moreover, the reduced droplet drift and improved canopy penetration contribute to better environmental protection. We also compared our results with existing studies on spraying UAVs, noting that similar trends have been observed in other crops, though the optimal parameters may vary due to differences in plant architecture and growth conditions. This underscores the importance of localized research for maximizing the benefits of crop spraying drone technology.
In conclusion, this study provides a robust framework for optimizing the operational parameters of crop spraying drones in the Yongzhou region. Through orthogonal experiments and detailed statistical analysis, we have identified that flight height, flight speed, and spray flow rate collectively influence droplet deposition density, with flight height being particularly critical for middle and lower canopy coverage. The recommended parameters—flight height of 2.1 m, flight speed of 4 m/s, and spray flow rate of 2.7 L/min—strike a balance between efficacy and efficiency, making them suitable for widespread adoption in seedling vegetable production. As spraying UAVs continue to evolve, further research could explore additional factors such as nozzle type and environmental variables, paving the way for even more precise and sustainable agricultural practices. Ultimately, the insights gained here contribute to the advancement of precision agriculture, empowering farmers to leverage crop spraying drone technology for improved crop health and yield.