Cypress leaf blight, a devastating disease affecting various species of the genus Platycladus, has emerged as a significant threat to forest ecosystems in arid and semi-arid regions. The disease rapidly spreads, causing extensive foliage yellowing, defoliation, and even tree mortality, leading to substantial ecological and economic losses. Traditional control methods, such as manual spraying, are often inefficient, costly, and impractical in rugged terrains with high tree density. In recent years, the adoption of crop spraying drone technology has revolutionized pest and disease management in forestry. These spraying UAV systems offer precision, efficiency, and environmental benefits, making them ideal for large-scale applications. This study focuses on evaluating the efficacy of different pesticides applied via crop spraying drone to control cypress leaf blight, aiming to identify optimal chemical formulations and concentrations for effective disease suppression.
The pathogen responsible for cypress leaf blight primarily infects new shoots and scales, with symptoms becoming apparent in early spring. Infected trees exhibit yellowing of scale leaves from the base upward, progressing rapidly to entire crown dieback within 10–15 days. The fungus overwinters as mycelia on infected tissues, and ascospores are released during rainy periods in late spring, facilitating wind- and rain-driven dispersal. This cycle results in a single annual infection event, emphasizing the critical need for timely intervention. Given the challenges of manual control in dense, inaccessible forests, spraying UAV technology provides a viable solution by enabling uniform pesticide coverage and reducing chemical waste. This article details a comprehensive experiment conducted to screen pesticides for crop spraying drone applications, incorporating statistical analyses, tables, and mathematical models to validate findings.
The disease development follows a distinct phenological pattern, which can be leveraged for predictive management. For instance, initial symptoms coincide with the bud-breaking stage of apricot trees in early March, while the peak disease occurrence aligns with the flowering period of specific plants in late April. Ascospore release peaks around mid-June, corresponding to the fruit-setting stage of apricots and the flowering of pomegranates. This correlation allows for precise timing of chemical interventions using crop spraying drone systems. The experimental design accounted for these phenological cues to maximize the impact of pesticide applications during the ascospore release period. By integrating meteorological data and tree phenology, we developed a predictive model for optimal spraying UAV deployment, enhancing the efficacy of control measures.
To quantify disease severity and treatment effects, we employed a disease index calculation based on the proportion of infected trees across severity grades. The formula for the disease index (DI) is defined as:
$$ DI = \frac{\sum_{i=1}^{n} (s_i \times n_i)}{N \times S_{max}} \times 100 $$
where \( s_i \) represents the severity score of grade \( i \), \( n_i \) is the number of trees in grade \( i \), \( N \) is the total number of trees assessed, and \( S_{max} \) is the maximum severity score (e.g., 5 for the highest grade). This index allows for a standardized comparison of disease levels before and after treatment. The reduction in disease index (\( \Delta DI \)) is calculated as:
$$ \Delta DI = DI_{\text{before}} – DI_{\text{after}} $$
and the control efficacy (CE) is expressed as:
$$ CE = \frac{DI_{\text{before}} – DI_{\text{after}}}{DI_{\text{before}}} \times 100\% $$
These metrics were central to evaluating the performance of different pesticides applied via crop spraying drone.
The experiment was conducted in pure cypress plantations characterized by varying tree heights (3–16 m) and spacing (approximately 1.5 m × 2.0 m), with trees aged over 15 years. Three distinct forest areas were selected for the trial, each treated with one of the following pesticides: 70% methyl thiophanate wettable powder, 60% methyl thiophanate–iprodione wettable powder, and 12.5% tebuconazole emulsion. Each pesticide was tested at three concentrations: 500×, 800×, and 1000× dilutions, with a control plot receiving water only. The spraying UAV used was a DJI T50 model, chosen for its high payload capacity and precision. Key parameters of the crop spraying drone are summarized in Table 1.
| Parameter | Value |
|---|---|
| Maximum Takeoff Weight | 78 kg |
| Standard Takeoff Weight | 66.5 kg |
| Maximum Spraying Flow Rate | 8 L/min |
| Hovering Time (at 36.5 kg) | 20.5 min |
| Hovering Time (at 66.5 kg) | 7.8 min |
| Maximum Operational Radius | 2000 m |
| Maximum Operational Speed | 7 m/s |
| Maximum Horizontal Speed | 10 m/s |
| Maximum Wind Resistance | 6 m/s |
| Maximum Flight Altitude | 4500 m |
| Operating Humidity | <93% |
| Operating Temperature | 0–45 °C |
Applications were made twice during the ascospore release period (early to mid-June), with a 7-day interval between treatments. Disease assessments were conducted before the first application and 30 days after the final application, recording the number of infected trees and calculating the disease index. The experimental layout ensured statistical robustness, with each treatment replicated across multiple plots. Data analysis involved comparing the reduction in disease index across treatments, followed by an analysis of variance (ANOVA) to determine significant differences. The use of spraying UAV technology ensured consistent droplet distribution and minimal environmental impact, as the drone’s advanced navigation system enabled precise flight paths and uniform coverage.
The results demonstrated that all tested pesticides significantly reduced disease severity compared to the control. However, 70% methyl thiophanate wettable powder at 500× dilution yielded the highest disease index reduction, indicating superior efficacy. The performance of each pesticide and concentration is detailed in Table 2, which summarizes the pre- and post-treatment disease indices and the calculated reductions. The data reveal a clear concentration-dependent effect, with higher dilutions (e.g., 1000×) showing diminished control. This underscores the importance of optimizing pesticide concentrations for crop spraying drone applications to balance efficacy and environmental safety.
| Pesticide | Concentration | Number of Trees Assessed | Disease Index Before | Disease Index After | Disease Index Reduction |
|---|---|---|---|---|---|
| 70% Methyl Thiophanate | 500× | 142 | 53.9 | 7.3 | 46.6 |
| 70% Methyl Thiophanate | 800× | 136 | 45.6 | 8.2 | 37.4 |
| 70% Methyl Thiophanate | 1000× | 108 | 40.1 | 11.3 | 28.8 |
| 60% Methyl Thiophanate–Iprodione | 500× | 131 | 46.4 | 16.9 | 29.5 |
| 60% Methyl Thiophanate–Iprodione | 800× | 112 | 36.8 | 12.0 | 24.8 |
| 60% Methyl Thiophanate–Iprodione | 1000× | 127 | 42.1 | 20.0 | 22.1 |
| 12.5% Tebuconazole | 500× | 134 | 50.9 | 16.1 | 34.8 |
| 12.5% Tebuconazole | 800× | 119 | 39.5 | 18.2 | 21.3 |
| 12.5% Tebuconazole | 1000× | 142 | 32.2 | 13.5 | 18.7 |
| Control (Water) | — | 120 | 39.8 | 51.7 | -11.9 |
Statistical analysis confirmed the significance of these findings. An ANOVA model was applied to the disease index reduction values, with the pesticide type and concentration as factors. The model can be expressed as:
$$ \Delta DI_{ij} = \mu + \alpha_i + \beta_j + (\alpha\beta)_{ij} + \epsilon_{ij} $$
where \( \Delta DI_{ij} \) is the disease index reduction for the \( i \)-th pesticide and \( j \)-th concentration, \( \mu \) is the overall mean, \( \alpha_i \) is the effect of the \( i \)-th pesticide, \( \beta_j \) is the effect of the \( j \)-th concentration, \( (\alpha\beta)_{ij} \) is the interaction effect, and \( \epsilon_{ij} \) is the random error. The results indicated significant main effects for both pesticide type and concentration (p < 0.01), with no significant interaction, suggesting that the concentration response is consistent across pesticides. Post-hoc tests, such as Tukey’s HSD, revealed that 70% methyl thiophanate at 500× dilution outperformed all other treatments, making it the recommended option for spraying UAV applications.
Beyond chemical control, integrated management strategies are essential for sustainable disease suppression. Preventive measures include selecting resistant tree varieties, implementing mixed-species plantations to reduce disease pressure, and applying silvicultural practices like pruning and thinning to improve air circulation and tree vigor. Additionally, the use of crop spraying drone technology can be enhanced by incorporating foliar fertilizers, such as potassium dihydrogen phosphate, to boost tree immunity. Predictive models based on phenological indicators enable proactive spraying UAV deployments, minimizing chemical usage and environmental impact. The advantages of crop spraying drone systems are multifaceted: they reduce labor costs, increase coverage efficiency in difficult terrains, and ensure precise chemical deposition. For instance, the droplet penetration and adhesion achieved by spraying UAV surpass traditional methods, leading to higher efficacy and lower residue levels.
To further optimize crop spraying drone operations, we developed a cost-benefit model that considers factors such as pesticide volume, flight time, and disease reduction. The total cost \( C \) per hectare can be estimated as:
$$ C = C_p + C_d + C_l $$
where \( C_p \) is the pesticide cost, \( C_d \) is the spraying UAV depreciation and maintenance cost, and \( C_l \) is the labor cost. The benefit \( B \) is derived from the reduction in economic losses due to disease, quantified as:
$$ B = \Delta DI \times V \times A $$
where \( \Delta DI \) is the average disease index reduction, \( V \) is the economic value per tree, and \( A \) is the area treated. The net benefit \( NB \) is then \( NB = B – C \). Our analysis showed that using 70% methyl thiophanate at 500× dilution via crop spraying drone yielded the highest net benefit, justifying its large-scale adoption.
In conclusion, this study highlights the transformative potential of crop spraying drone technology in managing cypress leaf blight. Through rigorous experimentation and statistical analysis, we identified 70% methyl thiophanate wettable powder at 500× dilution as the most effective treatment, with alternative pesticides suitable for rotation to prevent resistance. The integration of spraying UAV systems into forestry practices not only enhances control efficacy but also promotes environmental sustainability by reducing chemical runoff and operator exposure. Future research should focus on refining predictive models, exploring biopesticides for crop spraying drone applications, and automating flight paths using AI-based algorithms. By embracing these innovations, we can achieve long-term forest health and resilience, ensuring the preservation of vital ecosystems against emerging disease threats.