Pine wilt disease, caused by the pine wood nematode, stands as one of the most devastating forest diseases globally, threatening pine ecosystems with rapid spread and high mortality rates. Effective removal of infected dead wood is a core control measure, critical for halting epidemic expansion. However, traditional manual transportation in mountainous and remote terrains faces severe inefficiencies, with labor costs consuming over 50% of total cleanup expenses and leaving inaccessible areas untreated. To address this, we explored the application of agricultural drones, specifically evaluating their operational efficacy in infected wood removal. Our study demonstrates that agricultural drones significantly enhance efficiency and reduce costs, offering a transformative solution for forestry pest management.
In our field experiments, we employed the DJI T70 agricultural UAV, a model optimized for heavy payloads and precision operations. The experimental site was located in a mountainous forest region characterized by steep slopes (elevation range: 139–192 m) and sparse tree distribution. Target trees were pre-marked pine trees killed by pine wilt disease, randomly scattered across the terrain. We standardized the workflow: trees were felled and cut into 1–2 m segments, with branches bundled into 50 kg loads for consistent agricultural drone handling. The agricultural UAV operated in manual flight mode, with real-time communication via walkie-talkies ensuring safety in demarcated zones. Key parameters like flight distance, payload weight, and time per sortie were meticulously recorded. For detailed technical specifications of our setup, refer to the equipment documentation here.
We conducted 761 drone-assisted removals, with data revealing substantial performance gains. The agricultural drone achieved an average payload of 55.3 kg per sortie, completing each mission in under 4 minutes and 30 seconds. Daily operations peaked at 57 trees transported, showcasing unparalleled efficiency in complex landscapes. Comparatively, traditional manual methods managed only 23 trees per day in similar conditions. The efficiency improvement is quantified as:
$$ \text{Efficiency Improvement} = \frac{\text{Drone Output} – \text{Manual Output}}{\text{Manual Output}} \times 100\% = \frac{37.1 – 23.4}{23.4} \times 100\% = 58.8\% $$
Cost reduction was equally significant, calculated as:
$$ \text{Cost Savings} = \frac{\text{Manual Cost} – \text{Drone Cost}}{\text{Manual Cost}} \times 100\% = 23.5\% $$
Table 1 summarizes critical agricultural UAV performance metrics, highlighting its adaptability to elevation variations and payload consistency.
| Parameter | Range | Average |
|---|---|---|
| Flight Distance | 372–490 m | 431 m |
| Vertical Elevation | 139–192 m | 165.5 m |
| Payload Weight | 35.2–75.5 kg | 55.3 kg |
| Sortie Time | 3’45″–5’15” | 4’30” |
| Daily Sorties | 53–76 | 64.5 |
| Daily Tree Transport | 23–57 | 40 |
Comparative analysis against manual methods underscores the agricultural drone’s superiority in challenging environments. In steep areas, the agricultural UAV maintained high productivity, while manual efficiency dropped by over 60% due to terrain constraints. The agricultural drone’s intelligent obstacle avoidance enabled access to remote zones unreachable by humans, reducing pathogen retention risks. However, limitations include high initial investment and weather susceptibility. Table 2 contrasts the two approaches, advocating for an integrated model.
| Factor | Agricultural UAV | Manual Transport |
|---|---|---|
| Max Daily Output | 57 trees | 23 trees |
| Terrain Adaptability | Excellent (slopes >30°) | Poor (efficiency decline >60%) |
| Cost Efficiency | 23.5% savings | High labor costs |
| Accessibility | Precision in remote areas | Road-dependent |
| Limitations | Weather sensitivity, high CAPEX | Aging workforce, safety risks |
Our findings confirm that agricultural drones revolutionize pine wilt disease management by enabling complete wood removal, which is vital for eradicating pest vectors like Monochamus alternatus. The agricultural UAV’s speed and payload capacity mitigate the risk of epidemic resurgence from untreated trees. Yet, current constraints such as battery life (limiting sorties to 1–3 per charge) and operational costs necessitate optimization. We propose a hybrid system combining agricultural drones for inaccessible terrains and manual labor for flat areas, maximizing cost-benefit ratios. The hybrid efficiency can be modeled as:
$$ \text{Hybrid Efficiency} = \alpha \cdot E_d + (1 – \alpha) \cdot E_m $$
where \( E_d \) is drone efficiency (37.1 trees/day), \( E_m \) is manual efficiency (23.4 trees/day), and \( \alpha \) represents the drone deployment ratio (0 ≤ α ≤ 1).
Looking ahead, advancements in agricultural UAV technology promise greater impacts. Enhancing battery energy density could extend flight times beyond 30 minutes and increase payloads to 100+ kg, with the payload-flight time relationship expressed as:
$$ T = k \cdot \frac{E_b}{P} $$
Here, \( T \) is flight time, \( E_b \) is battery energy, \( P \) is power consumption, and \( k \) is a drone-specific constant. Integrating AI for autonomous navigation and IoT for real-time monitoring could expand agricultural drone roles to include early disease detection and precision spraying. Future research should focus on weather-resilient designs and cost reductions to democratize access. Ultimately, agricultural UAVs represent a sustainable paradigm shift, potentially reducing global pine wilt incidence by 30–40% through scalable, efficient interventions. As we refine these systems, the fusion of agricultural drone capabilities with ecological strategies will be pivotal for forest conservation.