In recent years, the global agricultural sector has been rapidly advancing toward precision and intelligence, driven by the need for sustainable practices. Traditional crop protection methods, such as manual spraying and ground-based machinery, often suffer from inefficiencies, high labor intensity, and significant pesticide waste, making them inadequate for modern farming demands. As a solution, crop spraying drones, also known as spraying UAVs, have emerged as a focal point in research and application for pest and disease control. These spraying UAVs offer high operational efficiency and precise pesticide application, addressing key limitations of conventional approaches. In this study, we evaluate the performance of crop spraying drones, including models like the DJI T40, by comparing them with traditional manual spraying and modern ground-based methods. We focus on aspects such as operational efficiency, spray quality, and environmental impact, providing insights based on real-world scenarios and data analysis. The integration of spraying UAVs into agricultural practices represents a shift toward smarter, more resource-efficient farming, aligning with global trends in low-carbon and precision agriculture.
The adoption of crop spraying drones has seen exponential growth worldwide, with China leading as the largest market for agricultural UAVs. By 2024, the global fleet of crop spraying drones exceeded 200,000 units, with an annual growth rate of approximately 14%. This expansion is largely fueled by government policies, such as subsidies for agricultural machinery, which have integrated spraying UAVs into national support programs. For instance, in China, subsidies cover up to 30% of the purchase cost, accelerating the penetration of these technologies in major grain-producing regions. Technologically, modern crop spraying drones are equipped with AI-driven variable rate application systems, centimeter-level positioning, and dynamic adjustment capabilities, enabling them to reduce pesticide usage by an average of 30% compared to traditional methods. However, despite these advancements, several challenges persist. Data standardization remains a critical issue, as formats for indices like NDVI (Normalized Difference Vegetation Index) and pest distribution maps vary between manufacturers, hindering cross-platform data sharing. Additionally, comprehensive cost-benefit analyses are lacking, particularly regarding battery lifecycle costs, operator training, and the impact of policy fluctuations. Environmental assessments also need depth, as current studies focus primarily on pesticide efficiency while overlooking aspects like battery disposal and energy consumption. Furthermore, regulatory oversight on operational quality, such as spray uniformity and operator certification, requires strengthening to ensure safe and effective deployment of spraying UAVs.
To assess the operational efficiency of crop spraying drones, we conducted a comparative analysis under a fixed service cost of 50 USD per hectare, evaluating various methods including traditional manual spraying, backpack electric sprayers, boom sprayers, and orchard air-assisted mist sprayers. The crop spraying drone used in this evaluation was the DJI T40, a representative model with a payload capacity of 20–30 kg and an operational coverage of 20.0–26.7 hectares per 8-hour day. Efficiency was measured in terms of daily area coverage, labor requirements, and time efficiency, with results summarized in the following tables. The efficiency multiple for each comparison was calculated using the formula: $$ \text{Efficiency Multiple} = \frac{\text{Daily Area of Spraying UAV}}{\text{Daily Area of Other Method}} $$ This highlights the advantages of spraying UAVs in diverse agricultural settings.
| Indicator | Spraying UAV (DJI T40) | Manual Spraying (5-person team) | Efficiency Multiple |
|---|---|---|---|
| Daily Area (hectares) | 20.0–26.7 | 1.7 | 12–15 |
| Labor Requirement | 1 person (pilot and observer) | 5 people | -80% |
As shown in the table, the crop spraying drone demonstrates a significant efficiency advantage, covering 12 to 15 times the area of a manual team while reducing labor by 80%. This makes spraying UAVs ideal for large-scale farms, where speed and labor savings are critical. Moreover, the use of a spraying UAV minimizes human exposure to pesticides, enhancing safety.
| Indicator | Spraying UAV (DJI T40) | Backpack Electric Sprayer (5-person team) | Efficiency Multiple |
|---|---|---|---|
| Daily Area (hectares) | 20.0–26.7 | 3.3–5.0 | 4–8 |
| Labor Requirement | 1 person (pilot and observer) | 5 people | -80% |
In comparison to backpack electric sprayers, the spraying UAV maintains a higher efficiency multiple of 4 to 8, despite the improved performance of electric sprayers over manual methods. This underscores the role of spraying UAVs in optimizing resource use and reducing operational time.
| Indicator | Spraying UAV (DJI T40) | Boom Sprayer (12m width) | Efficiency Multiple |
|---|---|---|---|
| Daily Area (hectares) | 20.0–26.7 | 40.0 | 0.5–0.67 |
| Turnaround Time | 5 minutes | 30 minutes | 6 |
| Labor Requirement | 1 person (pilot and observer) | 1 person (driver) | 1 |
When compared to boom sprayers, the spraying UAV shows a lower daily area coverage but excels in turnaround time, being six times faster. This makes crop spraying drones more adaptable to fragmented fields requiring frequent moves, whereas boom sprayers are better suited for large, contiguous areas with minimal obstacles.
| Indicator | Spraying UAV (DJI T40) | Orchard Air-Assisted Mist Sprayer | Efficiency Multiple |
|---|---|---|---|
| Daily Area (hectares) | 6.7–10.0 | 1.3 | 5–7.5 |
| Labor Requirement | 1 person (pilot and observer) | 2 people (operator and assistant) | -50% |
In orchard applications, the spraying UAV achieves an efficiency multiple of 5 to 7.5 over air-assisted mist sprayers, with half the labor requirement. However, the effectiveness of a crop spraying drone in such settings depends heavily on the AI algorithms for navigation and spray logic, which must be optimized for tree canopies to ensure uniform coverage.
Beyond operational efficiency, the quality of pest and disease control is a crucial metric for evaluating spraying UAVs. We assessed parameters such as leaf surface adhesion rate, leaf back adhesion rate, drift prevention, and over-spraying control. The adhesion rates were calculated using the formula: $$ \text{Adhesion Rate} = \frac{\text{Amount of Pesticide on Leaf}}{\text{Total Pesticide Applied}} \times 100\% $$ Similarly, drift rate was defined as: $$ \text{Drift Rate} = \frac{\text{Pesticide Lost to Environment}}{\text{Total Pesticide Applied}} \times 100\% $$ The following table summarizes the comparative performance across different methods, highlighting the strengths of crop spraying drones in precision application.
| Quality Parameter | Spraying UAV | Manual Spraying | Backpack Electric Sprayer | Boom Sprayer | Orchard Mist Sprayer |
|---|---|---|---|---|---|
| Leaf Surface Adhesion Rate (%) | 80–90 | 70–80 | 75–85 | 85–95 | 90–95 |
| Leaf Back Adhesion Rate (%) | 60–65 | 70–75 | 55–70 | 40–50 | 80–90 |
| Drift Rate (%) | < 5 | 10–20 | 15–25 | 10–25 | 5–15 |
| Spraying Error (%) | < 5 | 20–30 | 20–30 | 10–15 | 10–15 |
The data indicates that spraying UAVs achieve high leaf surface adhesion rates of 80–90%, comparable to boom sprayers and orchard mist sprayers, but lower than the latter in leaf back adhesion. This is due to the downwash airflow generated by the spraying UAV, which enhances penetration but may not fully reach the undersides of leaves in dense canopies. In terms of drift prevention, crop spraying drones perform exceptionally well, with a drift rate below 5%, significantly lower than other methods. This reduces environmental contamination and non-target exposure. Additionally, the precision of spraying UAVs results in minimal spraying errors (less than 5%), thanks to technologies like flow sensors and AI-based route planning. In contrast, manual and backpack methods exhibit higher errors of 20–30%, primarily due to human variability and lack of automated control.
In conclusion, crop spraying drones represent a transformative technology in agricultural pest and disease control, offering substantial benefits in efficiency, precision, and environmental sustainability. The spraying UAV excels in operational speed, labor reduction, and adaptability to various terrains, while its precision application minimizes pesticide waste and drift. However, challenges such as limited battery life, data interoperability issues, and the need for standardized regulations must be addressed to fully realize the potential of spraying UAVs. We recommend further research into battery technology, development of universal data protocols, and enhanced training programs for operators. As the industry evolves, spraying UAVs are poised to become integral to smart farming systems, contributing to increased productivity and ecological balance. By leveraging the capabilities of crop spraying drones, farmers can achieve more effective and sustainable pest management, aligning with the global push for agricultural innovation.