In modern agriculture, the use of unmanned aerial vehicles (UAVs), particularly agricultural drones, has revolutionized pest and disease management. These aerial platforms offer precision, efficiency, and adaptability, especially in challenging terrains like rubber plantations. Rubber trees, vital for natural rubber production, are susceptible to foliar diseases such as powdery mildew and anthracnose, which can cause significant yield losses if not managed effectively. Traditional control methods, such as manual spraying or dusting, are labor-intensive, inefficient, and often result in uneven chemical distribution. This study focuses on leveraging a heavy-load agricultural drone to optimize spraying parameters and evaluate the efficacy of various chemical agents against these diseases. By integrating advanced UAV technology with tailored agrochemicals, we aim to develop a robust aerial defense system that enhances crop protection while reducing environmental impact. The findings presented here contribute to the growing body of knowledge on agricultural drone applications, paving the way for sustainable and smart farming practices.
The adoption of agricultural drones in crop protection has gained momentum due to their ability to cover large areas quickly and apply chemicals with high precision. For rubber trees, which have tall canopies and dense foliage, conventional ground-based spraying often fails to penetrate the lower layers, leading to inadequate disease control. Heavy-load agricultural drones, with their greater payload capacity and stronger downwash airflow, offer a promising solution. In this research, we employed a FBH300T dual-rotor heavy-load agricultural drone to investigate the effects of flight altitude and application volume on droplet deposition on rubber tree leaves. Additionally, we tested several chemical agents, both singly and in combinations, to assess their control efficacy against powdery mildew and anthracnose. The optimization of spraying parameters is critical for maximizing droplet coverage and penetration, which directly influences the success of aerial applications. Through systematic experimentation and analysis, we identified optimal settings for the agricultural drone and evaluated the performance of novel and conventional pesticides. This comprehensive approach not only enhances our understanding of aerial spray dynamics but also provides practical insights for implementing agricultural drone technology in rubber plantations. As the demand for efficient and eco-friendly farming practices grows, agricultural drones are poised to become indispensable tools in integrated pest management strategies.
The study was conducted in two experimental sites in Guangdong Province, China, focusing on mature rubber trees of the cultivar ‘Reyan 7-33-97’. The first site was used for droplet deposition tests, while the second site was dedicated to efficacy evaluations of chemical agents. The agricultural drone used was the FBH300T, a heavy-load model with a dual-rotor configuration, powered by a gasoline engine. Key specifications of this agricultural drone are summarized in Table 1. This agricultural drone was chosen for its high payload capacity and strong downwash, which are essential for penetrating the dense canopy of rubber trees. The flight parameters, including altitude above canopy and application volume, were varied to assess their impact on droplet distribution. We used water-sensitive paper cards placed at upper and lower leaf layers to capture droplet deposits, which were then analyzed using image processing software. The experimental design followed an orthogonal approach to efficiently evaluate multiple factors. For the chemical trials, we selected four agents: 19% Baoyeqing ME, 60% sulfur SC, 45% tebuconazole·imidazole EW, and 50% sulfur·triadimefon SC. These were applied alone or in mixtures using the optimized agricultural drone settings. Disease incidence and severity were recorded post-application to calculate control efficacy. All data were subjected to statistical analysis to determine significant differences and optimal combinations.
| Parameter | Value |
|---|---|
| Model | FBH300T |
| Rotor Diameter (m) | 3.2 |
| Maximum Payload (kg) | 140 |
| Engine Power (W) | 47775 |
| Cruising Speed (km/h) | 20-80 |
| Flight Endurance (h) | 1-3 |
| Spray System | Dual nozzles, 100 μm droplet size |
Droplet deposition is a key metric for evaluating the performance of an agricultural drone. The density and coverage of droplets on target surfaces determine the effectiveness of chemical delivery. In this study, we defined droplet density (D) as the number of droplets per unit area, calculated using the formula: $$D = \frac{N}{A}$$ where \(N\) is the total number of droplets and \(A\) is the area of the water-sensitive card in cm². Droplet coverage (C) represents the percentage of area covered by droplets: $$C = \frac{A_c}{A_t} \times 100\%$$ where \(A_c\) is the area covered by droplets and \(A_t\) is the total area. We tested three flight altitudes (3 m, 5 m, and 7 m above the canopy) and three application volumes (60 L/ha, 90 L/ha, and 105 L/ha) in a full factorial design. The agricultural drone was flown at a constant speed of 5 m/s. Each treatment was replicated, and data were collected from both upper and lower leaf layers. The results, presented in Table 2, show that both altitude and volume significantly influenced droplet deposition. The highest droplet density and coverage were observed at 5 m altitude with 90 L/ha, indicating an optimal balance between spray penetration and drift reduction. Statistical analysis confirmed the significance of these factors, with p-values less than 0.01. The downwash airflow generated by the agricultural drone helped in dispersing droplets evenly, but excessive altitude reduced deposition due to increased drift. This underscores the importance of parameter optimization for each agricultural drone model and crop type.
| Altitude (m) | Volume (L/ha) | Droplet Density (droplets/cm²) | Droplet Coverage (%) |
|---|---|---|---|
| 3 | 60 | 32.47 ± 1.07 | 3.29 ± 0.02 |
| 90 | 40.93 ± 0.74 | 4.12 ± 0.01 | |
| 105 | 22.70 ± 0.40 | 2.24 ± 0.07 | |
| 5 | 60 | 27.33 ± 1.00 | 2.81 ± 0.04 |
| 90 | 39.53 ± 0.76 | 4.01 ± 0.07 | |
| 105 | 34.43 ± 1.11 | 3.28 ± 0.06 | |
| 7 | 60 | 21.47 ± 1.06 | 2.29 ± 0.07 |
| 90 | 30.03 ± 1.68 | 2.84 ± 0.07 | |
| 105 | 31.40 ± 0.72 | 3.37 ± 0.05 |
The optimization process involved range analysis to identify the primary factors affecting deposition. For droplet density, the application volume had a greater influence than flight altitude, with an optimal level of 90 L/ha. Similarly, for coverage, volume was the dominant factor. The interaction between altitude and volume was also significant, highlighting the need for integrated parameter tuning. Based on these findings, we recommend a flight altitude of 5 m above canopy and an application volume of 90 L/ha for the FBH300T agricultural drone when treating rubber trees. These settings ensure adequate droplet density and coverage across both upper and lower leaves, maximizing the potential for disease control. The use of an agricultural drone with these optimized parameters can significantly improve chemical utilization and reduce waste, aligning with sustainable agriculture goals. Furthermore, the strong downwash of the heavy-load agricultural drone enhances canopy penetration, a critical aspect for tall crops like rubber trees. This optimization serves as a foundation for subsequent efficacy trials with chemical agents.
Following the parameter optimization, we evaluated the control efficacy of various chemical agents against rubber tree powdery mildew and anthracnose. The agricultural drone was used to apply the chemicals at the optimized settings: 5 m altitude, 90 L/ha volume, and 5 m/s speed. Seven treatments were tested, including single agents and mixtures, with water as a control. Disease assessment was conducted after two applications, and the relative control efficacy (E) was calculated using the formula: $$E = \left(1 – \frac{DI_t}{DI_c}\right) \times 100\%$$ where \(DI_t\) is the disease index of the treatment and \(DI_c\) is the disease index of the control. The results, summarized in Table 3, show that all treatments provided significant disease reduction compared to the control. For powdery mildew, the mixture of 19% Baoyeqing ME and 60% sulfur SC at a 1:1 ratio achieved the highest efficacy of 82.35%, followed by the mixture of 19% Baoyeqing ME and 50% sulfur·triadimefon SC at 1:1 (80.05%). Among single agents, 50% sulfur·triadimefon SC performed best against powdery mildew, while 19% Baoyeqing ME was most effective against anthracnose. These findings demonstrate the potential of tailored chemical combinations when delivered via an agricultural drone. The aerial application ensured uniform coverage, which likely contributed to the high efficacy levels. Moreover, the use of mixtures can delay the development of pesticide resistance, a common issue in monoculture plantations.
| Treatment | Chemical Agent | Powdery Mildew Efficacy (%) | Anthracnose Efficacy (%) |
|---|---|---|---|
| 1 | 19% Baoyeqing ME | 71.66 ± 0.62 | 82.78 ± 0.05 |
| 2 | 60% sulfur SC | 70.35 ± 1.47 | 31.02 ± 0.31 |
| 3 | 45% tebuconazole·imidazole EW | 64.04 ± 0.33 | 70.33 ± 0.94 |
| 4 | 50% sulfur·triadimefon SC | 76.62 ± 0.85 | 66.24 ± 0.33 |
| 5 | 19% Baoyeqing ME + 60% sulfur SC (1:1) | 82.35 ± 0.17 | 77.26 ± 0.37 |
| 6 | 19% Baoyeqing ME + 45% tebuconazole·imidazole EW (2:1) | 69.41 ± 0.76 | 81.41 ± 0.54 |
| 7 | 19% Baoyeqing ME + 50% sulfur·triadimefon SC (1:1) | 80.05 ± 0.28 | 79.32 ± 0.04 |
The superior performance of mixtures can be attributed to synergistic effects between active ingredients. For instance, sulfur-based compounds are known for their protective action against powdery mildew, while triazole derivatives like tebuconazole offer systemic control. When combined, they provide broad-spectrum protection with multiple modes of action. The agricultural drone facilitated the even distribution of these mixtures, ensuring that both upper and lower leaves received adequate dosage. This is particularly important for diseases like anthracnose, which often infect lower canopy layers. The use of an agricultural drone also reduced chemical usage compared to traditional methods. For example, the application volume of 90 L/ha is substantially lower than the typical rates used in manual spraying, which can exceed 200 L/ha. This reduction not only cuts costs but also minimizes environmental contamination. The integration of optimized agricultural drone parameters with effective chemical agents represents a significant advancement in precision agriculture for rubber plantations.
Beyond efficacy, the economic and environmental benefits of using an agricultural drone are noteworthy. The FBH300T heavy-load agricultural drone can cover large areas quickly, reducing labor requirements and operational time. In our trials, the drone treated approximately 1 hectare per flight, with each flight lasting around 30 minutes. This efficiency is crucial during disease outbreaks, when timely intervention is essential. Additionally, the precision of the agricultural drone minimizes off-target drift, protecting non-target organisms and reducing chemical exposure for workers. The downwash airflow also helps in depositing droplets on the undersides of leaves, which are often missed by ground-based sprayers. These advantages make agricultural drones a valuable tool for sustainable crop management. However, successful implementation requires proper training and calibration. Pilots must understand the effects of weather conditions, such as wind and humidity, on spray patterns. Regular maintenance of the agricultural drone, including nozzle checks and battery management, is also critical for consistent performance. As technology advances, features like real-time monitoring and autonomous flight planning will further enhance the capabilities of agricultural drones.
In conclusion, this study demonstrates the potential of heavy-load agricultural drones in managing rubber tree diseases. Through systematic optimization, we identified that a flight altitude of 5 m above canopy and an application volume of 90 L/ha are optimal for the FBH300T agricultural drone. These settings ensure high droplet density and coverage across the tree canopy, improving chemical delivery. The evaluation of chemical agents revealed that mixtures, particularly 19% Baoyeqing ME with sulfur-based products, offer the best control against both powdery mildew and anthracnose. The use of an agricultural drone for application enhances efficacy while reducing chemical input and labor costs. These findings provide a practical framework for implementing agricultural drone technology in rubber plantations, contributing to more efficient and environmentally friendly disease management. Future research should focus on long-term field trials, integration with remote sensing for disease detection, and development of drone-specific formulations. As agricultural drones become more accessible, their adoption in tropical agriculture will likely expand, supporting global efforts toward food security and sustainable development. The continuous innovation in agricultural drone technology promises to transform traditional farming practices, making them smarter and more resilient.
The mathematical modeling of droplet deposition can be further refined to predict performance under varying conditions. For instance, the relationship between flight parameters and deposition can be expressed as: $$D = k \cdot \frac{V^a}{H^b}$$ where \(D\) is droplet density, \(V\) is application volume, \(H\) is flight altitude, and \(k\), \(a\), \(b\) are constants derived from empirical data. Such models can aid in simulating scenarios before actual field operations, optimizing resource use. Additionally, the control efficacy can be linked to deposition parameters through regression analysis. For example, a linear model might be: $$E = \beta_0 + \beta_1 D + \beta_2 C + \epsilon$$ where \(E\) is efficacy, \(D\) is density, \(C\) is coverage, \(\beta\) are coefficients, and \(\epsilon\) is error. These analytical approaches enhance the scientific basis for agricultural drone applications. In practice, the adoption of agricultural drones should be accompanied by farmer training and policy support. Governments and agricultural extensions can promote the use of agricultural drones through subsidies and technical workshops. Moreover, collaborations between research institutions and drone manufacturers can drive innovation, leading to more tailored solutions for crops like rubber trees. The versatility of agricultural drones also allows for their use in other farm operations, such as fertilization and pollination, maximizing return on investment. As we move forward, the integration of artificial intelligence and IoT with agricultural drones will enable real-time data-driven decisions, ushering in a new era of precision agriculture. The journey from manual spraying to automated aerial systems exemplifies the transformative power of technology in addressing agricultural challenges.
In summary, this research highlights the critical role of agricultural drones in modern crop protection. By optimizing flight parameters and selecting effective chemical agents, we can achieve superior disease control in rubber plantations. The heavy-load agricultural drone proved to be a reliable platform for aerial spraying, offering advantages in coverage, penetration, and efficiency. The findings encourage wider adoption of agricultural drone technology in tropical regions, where traditional methods are often inadequate. As we continue to explore the potential of agricultural drones, it is essential to address challenges such as regulatory hurdles, cost barriers, and technical skill gaps. Through collaborative efforts, agricultural drones can become a cornerstone of sustainable agriculture, ensuring healthy crops and prosperous farming communities. The future of farming is undoubtedly intertwined with the evolution of agricultural drones, and this study contributes a step toward that future.