As a vital staple crop, rice production quality and yield directly impact national food security. Traditional manual pesticide spraying struggles to address diverse rice pests and diseases efficiently. Agricultural drones offer transformative solutions with high efficiency and reduced environmental impact, supporting modernized rice production. This article explores their application value, targets, implementation methodologies, and future potential.
Application Value of Agricultural UAVs
Terrain Adaptability: Agricultural drones navigate complex rice field topographies—flat plains or terraced hills—with precision. Their maneuverability eliminates limitations of conventional machinery, ensuring comprehensive coverage. Flight stability enables consistent spray deposition regardless of landscape undulations, governed by:
$$ \text{Spray Uniformity} = 1 – \frac{\sum_{i=1}^{n} |D_i – \bar{D}|}{n \cdot \bar{D}} $$
where \(D_i\) is deposition at point \(i\), \(\bar{D}\) is mean deposition, and \(n\) is measurement points.
Resource Efficiency: Agricultural UAVs reduce chemical and water usage through targeted spraying. Advanced navigation systems optimize flight paths, minimizing overlap and drift. Ultra-Low Volume (ULV) spraying technology enhances utilization:
$$ \text{Chemical Savings} = \frac{Q_{\text{traditional}} – Q_{\text{drone}}}{Q_{\text{traditional}}} \times 100\% $$
where \(Q\) represents chemical volume per hectare.
| Parameter | Manual Spraying | Agricultural UAV | Reduction (%) |
|---|---|---|---|
| Water Usage (L/ha) | 450-600 | 15-30 | 93-97 |
| Pesticide Usage (L/ha) | 1.5-2.0 | 0.8-1.2 | 33-47 |
| Operational Efficiency (ha/hr) | 0.3-0.5 | 2.5-4.0 | 700-1200 |
Primary Targets for Agricultural Drone Interventions
Agricultural drones effectively combat major rice threats:
- Chilo suppressalis: Larvae bore into stems, causing dead hearts and white heads. UAVs deliver systemic insecticides to stem bases.
- Nilaparvata lugens: Sap-sucking migrants controlled via timely drone sprays during migration peaks.
- Rhizoctonia solani: Fungal sheath blight managed through foliar fungicides applied at tillering stage.
- Ustilaginoidea virens: False smut suppressed during booting stage with precision drone applications.
| Target | Growth Stage | Recommended Agent | Drone Height (m) | Spray Volume (L/ha) |
|---|---|---|---|---|
| Chilo suppressalis | Tillering | Chlorantraniliprole | 2.0-2.5 | 15-20 |
| Nilaparvata lugens | Heading | Pymetrozine | 1.8-2.2 | 12-18 |
| Rhizoctonia solani | Jointing | Validamycin | 2.2-2.6 | 20-25 |
| Ustilaginoidea virens | Booting | Tebuconazole | 2.0-2.3 | 15-20 |
Implementation Framework for Agricultural Drone Operations
Pre-Intervention Monitoring
Multispectral sensors on agricultural UAVs calculate vegetation indices to detect stress:
$$ \text{NDVI} = \frac{\rho_{\text{NIR}} – \rho_{\text{Red}}}{\rho_{\text{NIR}} + \rho_{\text{Red}}} $$
Threshold-based alerts trigger interventions when NDVI < 0.65 or differential indices exceed 15% from baseline.
Chemical Selection Protocol
| Chemical Type | Adjuvant Requirement | Droplet Size (µm) | Drift Potential |
|---|---|---|---|
| Systemic Insecticides | Polymer additives | 150-250 | Low |
| Contact Fungicides | Surfactants | 200-300 | Medium |
| Herbicides | Anti-evaporants | 300-400 | High |
Preventive Application Strategy
Time applications using Growing Degree Day (GDD) models:
$$ \text{GDD} = \sum \left( \frac{T_{\text{max}} + T_{\text{min}}}{2} – T_{\text{base}}} \right) $$
where \(T_{\text{base}}\) = 10°C for most rice pests. Spray at 450 GDD for Chilo suppressalis prevention.
Case Implementation Analysis
A 2024 early-season project demonstrated systematic agricultural drone deployment:
- Pre-Operational Phase: Service procurement via competitive bidding; zone selection using GIS mapping; spray planning accounting for topography and wind patterns
- Operational Execution: Time-synchronized spraying at 98% panicle emergence; real-time drone telemetry monitoring; mandatory flight logging with geotagged documentation
- Post-Operational Validation: Efficacy assessment at 7-day intervals; yield comparison between treated/control plots; economic analysis
Technical specifications and operational data: nan
| Phase | Duration (Days) | Key Activities | Agricultural UAV Utilization |
|---|---|---|---|
| Preparation | 15 | Field mapping, Path planning | 20 flight hours |
| Application | 5 | Chemical spraying | 120 flight hours |
| Evaluation | 30 | Disease scoring, Yield measurement | 15 scouting hours |
Efficacy Assessment Methodology
Control effectiveness quantifies agricultural UAV performance:
$$ \text{Efficacy} = \left[1 – \frac{\text{Disease Index}_{\text{treated}}}{\text{Disease Index}_{\text{control}}}} \right] \times 100\% $$
Economic assessment incorporates operational parameters:
$$ \text{ROI} = \frac{(Y_{\text{drone}} \times P) – C_{\text{drone}}}{(Y_{\text{traditional}} \times P) – C_{\text{traditional}}} \times 100\% $$
where \(Y\) = yield (kg/ha), \(P\) = market price ($/kg), \(C\) = control costs ($/ha).
Conclusion
Agricultural drones revolutionize rice protection through precision application, resource efficiency, and adaptive terrain operation. Integration of spectral monitoring, predictive modeling, and optimized spray protocols enhances sustainability. Future advancements in swarm intelligence and AI-driven decision systems will further establish agricultural UAVs as indispensable tools in modern integrated pest management frameworks, ensuring food security through technologically empowered agriculture.