1. Introduction
As a researcher immersed in precision agriculture, I have witnessed the transformative role of agricultural unmanned aerial vehicles (UAVs) in modernizing farming practices. These systems enhance productivity, enable real-time crop monitoring, and optimize resource allocation. This article synthesizes a decade (2013–2023) of Chinese research on agricultural unmanned aerial vehicles, leveraging bibliometric analysis to map trends, hotspots, and frontiers.
2. Methodology
Using CiteSpace 6.3.R1, I analyzed 1,492 publications from the CNKI database. Key parameters included:
- Time slicing: 2013–2023 (1-year intervals)
- Thresholds: Top 50 cited/keyword items per slice
- Pruning: Pathfinder and pruning sliced networks
Cluster significance was validated by: - Modularity (Q): 0.812 (>0.5 indicates significant structure)
- Silhouette (S): 0.948 (>0.7 indicates high reliability)
3. Temporal Evolution of Publications
Agricultural unmanned aerial vehicle research surged linearly (Figure 1), with three distinct phases:
Table 1: Annual Publication Growth
| Period | Avg. Annual Publications | Cumulative Share (%) |
|---|---|---|
| 2013–2015 | 18.33 | 1.23 |
| 2016–2021 | 250.17 | 70.31 |
| 2022–2023 | 174.83 | 28.46 |
The 2016–2021 boom reflects policy support (e.g., China’s “Digital Agriculture” initiatives) and technological affordability.
4. Research Hotspots and Keyword Analysis
Co-occurrence analysis (Figure 4) identified core themes. High-frequency keywords (Table 2) were ranked by centrality (impact on the research network):
Table 2: Top Keywords (2013–2023)
| Keyword | Frequency | Centrality | First Appearance |
|---|---|---|---|
| UAV | 552 | 0.49 | 2013 |
| Remote Sensing | 77 | 0.35 | 2014 |
| Precision Agriculture | 51 | 0.33 | 2014 |
| Pest Management | 40 | 0.29 | 2016 |
| Multispectral | 25 | 0.26 | 2021 |
Clustering (Figure 5) revealed thematic groups:
- Cluster #0: Agricultural Equipment (28 members)
- Cluster #1: Precision Crop Management (25 members)
- Cluster #2: Remote Sensing Integration (21 members)
5. Research Frontiers and Burst Detection
Keyword bursts (sudden frequency surges) signal emerging trends (Figure 7):
- Spray Technology (2016–2018; Strength=4.07): Focus on pesticide deposition optimization.
- Smart Agriculture (2021–2023; Strength=3.82): AI-driven decision-making.
- Multispectral Imaging (2021–2023; Strength=3.15): Crop health assessment via NDVI:
NDVI=NIR−RedNIR+RedNDVI=NIR+RedNIR−Red
where NIR = near-infrared reflectance, Red = visible red reflectance.
6. Institutional Collaboration Networks
Leading contributors included the Chinese Academy of Agricultural Sciences and China Agricultural University. Collaboration density remained low (network density=0.0148), indicating fragmented efforts.
7. Future Directions
Based on burst trends, future agricultural unmanned aerial vehicle research will prioritize:
A. AI-Driven Workflow Optimization
Autonomous path planning using reinforcement learning:Q(s,a)←Q(s,a)+α[r+γmaxa′Q(s′,a′)−Q(s,a)]Q(s,a)←Q(s,a)+α[r+γa′maxQ(s′,a′)−Q(s,a)]
where ss = state, aa = action, rr = reward, γγ = discount factor.
B. Hyperspectral-Multisensor Fusion
Integrating thermal, LiDAR, and hyperspectral sensors for 3D crop modeling:Canopy Height Model=DSM−DTMCanopy Height Model=DSM−DTM
DSM = digital surface model; DTM = digital terrain model.
C. Carbon Footprint Reduction
UAVs reduce agrochemical usage by 30–50% compared to traditional methods. Future studies must quantify environmental gains:Carbon Savings=∑(Fuel Savedi×EFi)Carbon Savings=∑(Fuel Savedi×EFi)
where EFiEFi = emission factor for input ii.
8. Conclusion
Over 2013–2023, Chinese agricultural unmanned aerial vehicle research evolved from foundational applications toward precision, intelligence, and sustainability. Core advancements include multispectral imaging, AI integration, and specialized pest management. Future breakthroughs hinge on interdisciplinary collaboration, sensor fusion, and scalable AI solutions. As this field matures, agricultural unmanned aerial vehicles will become indispensable for global food security and ecological resilience.