Vision-Guided Autonomous UAV Swarms for Precision Agrochemical Application Using Multispectral Crop Health Monitoring
Keywords:
autonomous UAV swarms, precision agriculture, multispectral monitoring, swarm coordination, variable-rate spraying, agricultural governance, socio-technical infrastructure, path planning, edge AI, sustainabilityAbstract
The intensification of global food demand and the environmental consequences of conventional agrochemical application necessitate a paradigm shift toward precision agriculture. This paper presents a comprehensive system-level analysis of vision-guided autonomous unmanned aerial vehicle (UAV) swarms that integrate multispectral crop health monitoring with coordinated pesticide and fertilizer spraying. Unlike prior work focusing on isolated technological components, this study addresses the structural trade-offs, architectural decisions, and socio-technical governance challenges inherent in deploying such swarms at scale. A layered system architecture is proposed, comprising a perception layer using multispectral cameras and onboard vision processors, a decision layer that fuses crop health indices with historical field data, and an execution layer where swarm path planning and chemical release are synchronized through distributed consensus protocols. Critical design tensions emerge between real-time responsiveness and energy efficiency, between local autonomy and global optimality, and between cost constraints and sensor resolution. The paper further explores infrastructure requirements for ground control stations, communication networks, and battery exchange logistics, as well as sustainability considerations including reduced chemical runoff, lower carbon emissions, and the rebound effects that may arise from increased operational efficiency. Governance implications are examined through the lens of equitable access for smallholder farmers, certification of autonomous systems, and liability frameworks for off-target drift. Cross-domain comparisons with autonomous maritime and ground vehicle swarms reveal valuable lessons for standardization and fail-safe mechanisms. A case illustration of a 50-hectare vineyard demonstrates how multispectral normalized difference vegetation index (NDVI) maps enable variable-rate spraying with swarm coordination, reducing agrochemical use by up to forty percent compared to uniform application. The paper concludes with forward-looking perspectives on integrating edge artificial intelligence, weather-adaptive mission planning, and decentralized ledger technologies for transparent supply chain auditing. The findings underscore that while technological capability is advancing rapidly, successful adoption hinges on robust infrastructure, inclusive governance, and the alignment of incentives across stakeholders.
References
1. Pimentel, D., & Burgess, M. (2014). Environmental and economic costs of the application of pesticides primarily in the United States. In Integrated pest management (pp. 47-71). Springer.
2. Pretty, J., & Bharucha, Z. P. (2015). Integrated pest management for sustainable intensification of agriculture in Asia and Africa. Insects, 6(1), 152-182.
3. Hunt, E. R., Jr., & Daughtry, C. S. T. (2018). What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture? International Journal of Remote Sensing, 39(15-16), 5345-5376.
4. Floreano, D., & Wood, R. J. (2015). Science, technology and the future of small autonomous drones. Nature, 521(7553), 460-466.
5. Valavanis, K. P., & Vachtsevanos, G. J. (Eds.). (2015). Handbook of unmanned aerial vehicles. Springer.
6. Zhou, D. (2025, October). Swarm Intelligence-Based Multi-UAV Cooperative Coverage and Path Planning for Precision Pesticide Spraying in Irregular Farmlands. In 2025 3rd International Conference on Artificial Intelligence and Automation Control (AIAC) (pp. 395-398). IEEE.
7. Thenkabail, P. S., Lyon, J. G., & Huete, A. (Eds.). (2018). Hyperspectral remote sensing of vegetation (2nd ed.). CRC Press.
8. Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 114(4), 358-371.
9. Roldán, J. J., del Cerro, J., & Barrientos, A. (2017). A framework for multi-UAV systems in precision agriculture. In Proceedings of the 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (pp. 1-6). IEEE.
10. Pinciroli, C., Trianni, V., O'Grady, R., Pini, G., Brutschy, A., Brambilla, M., ... & Dorigo, M. (2012). ARGoS: A modular, parallel, multi-engine simulator for multi-robot systems. Swarm Intelligence, 6(4), 271-295.
11. Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017, Article 1353691.
12. Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61-69.
13. Dias, M. B., Zlot, R., Kalra, N., & Stentz, A. (2006). Market-based multipobot coordination: A survey and analysis. Proceedings of the IEEE, 94(7), 1257-1270.
14. Faiçal, B. S., Costa, F. G., Pessin, G., Ueyama, J., Freitas, H., Colombo, A., ... & De Rosa, F. (2014). The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides. Journal of Network and Computer Applications, 43, 1-8.
15. Sahin, S., & Dil, M. A. (2020). Life cycle assessment of unmanned aerial vehicle-based pesticide spraying in agriculture. Journal of Cleaner Production, 267, 122069.
16. Ferroni, M., & Zhou, Y. (2021). Data governance in precision agriculture: A review of challenges and opportunities. Agricultural Systems, 190, 103111.
17. Clarke, R. (2014). Understanding the drone epidemic. Computer Law & Security Review, 30(3), 230-246.
18. Zhang, Y., & Kato, N. (2019). A survey on maritime unmanned aerial vehicle systems: Architecture, communication, and control. IEEE Communications Surveys & Tutorials, 21(4), 3268-3297.
19. Vasiliev, I., & Pshikhopov, V. (2020). Fail-safe control of autonomous unmanned aerial vehicles: A review. Progress in Aerospace Sciences, 117, 100636.
20. Lacey, G., & Taylor, G. W. (2020). Deep learning on edge devices for precision agriculture. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 84-85). IEEE.
21. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Data Intelligence and AI Systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
