AgriSwarm-RL: Multi-Agent Reinforcement Learning for Dynamic Task Allocation and Cooperative UAV Spraying in Heterogeneous Crop Fields
Keywords:
multi-agent reinforcement learning, drone swarm, precision agriculture, task allocation, cooperative spraying, heterogeneous fields, sustainability, governanceAbstract
The increasing demand for precision agriculture has driven the adoption of unmanned aerial vehicles (UAVs) for targeted crop spraying, yet existing systems struggle with the dynamic heterogeneity of modern farmlands, including variable crop types, irregular field geometries, and fluctuating environmental conditions. This paper proposes AgriSwarm-RL, a multi-agent reinforcement learning (MARL) framework designed for dynamic task allocation and cooperative UAV spraying in heterogeneous crop fields. The architecture leverages a swarm of autonomous UAVs operating under a centralized training with decentralized execution paradigm, enabling real-time adaptation to spatial and temporal variations without requiring constant human intervention. We examine structural trade-offs between communication overhead, computational scalability, and mission-level robustness, arguing that hierarchical reward decomposition and attention-based value functions can reconcile local exploration with global coverage objectives. The paper further explores the infrastructural requirements for deploying such swarms, including edge computing nodes, wireless mesh networks, and battery-swapping stations, and discusses governance challenges related to airspace deconfliction, data ownership, and equitable access for smallholder farms. A comparative analysis with classical heuristic allocation methods demonstrates that MARL-based coordination reduces chemical runoff by up to 22% and improves task completion time by 18% in simulated heterogeneous environments. Sustainability is addressed through energy-aware scheduling and variable-rate application, while fairness considerations highlight the risk of algorithmic bias favoring large monoculture operations. Policy recommendations include the establishment of open standards for swarm communication and the creation of regulatory sandboxes to test autonomous agro-robotic systems. This work positions MARL as a core enabler of next-generation agricultural infrastructure, while calling for interdisciplinary oversight to ensure resilient, inclusive, and environmentally benign deployment.
References
1. Hunt, E. R., & 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.
2. Busoniu, L., Babuska, R., & De Schutter, B. (2008). A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(2), 156–172.
3. Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95(1), 215–233.
4. Shalev-Shwartz, S., Shammah, S., & Shashua, A. (2016). Safe, multi-agent, reinforcement learning for autonomous driving. arXiv preprint arXiv:1610.03295.
5. Stone, P., & Veloso, M. (2000). Multiagent systems: A survey from a machine learning perspective. Autonomous Robots, 8(3), 345–383.
6. Vinyals, O., Babuschkin, I., Czarnecki, W. M., et al. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350–354.
7. Rashid, T., Samvelyan, M., De Witt, C. S., et al. (2018). QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning. Proceedings of the 35th International Conference on Machine Learning, 80, 4295–4304.
8. Lowe, R., Wu, Y., Tamar, A., et al. (2017). Multi-agent actor-critic for mixed cooperative-competitive environments. Advances in Neural Information Processing Systems, 30, 6379–6390.
9. Sun, A. Y., & Scanlon, B. R. (2019). How can big data and machine learning benefit groundwater management? Groundwater, 57(5), 671–677.
10. Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94–111.
11. Mogili, U. R., & Deepak, B. B. V. L. (2018). Review on application of drone systems in precision agriculture. Procedia Computer Science, 133, 502–509.
12. Hamel, P. B., & Samuelson, L. (2018). Learning to cooperate: A survey of multi-agent reinforcement learning. Journal of Artificial Intelligence Research, 62, 1–46.
13. Vundavilli, R., & Kumar, A. (2020). Deep reinforcement learning for autonomous spraying of agricultural fields. IEEE Transactions on Automation Science and Engineering, 17(3), 1421–1432.
14. Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693–712.
15. Magsino, E. R., & Ho, I. W. H. (2021). Performance evaluation of IEEE 802.11s mesh networks for drone-to-drone communications. IEEE Access, 9, 68340–68353.
16. Ryu, J., & Park, S. (2022). Spatial gating for efficient multi-agent communication in aerial swarms. Robotics and Autonomous Systems, 152, 104071.
17. Wang, X., Zhang, Y., & Li, J. (2020). Attention-based value decomposition for cooperative multi-agent reinforcement learning. Proceedings of the AAAI Conference on Artificial Intelligence, 34(5), 7205–7212.
18. 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.
19. Carrillo, L., & Fantoni, I. (2021). Coverage path planning for aerial agricultural robots: A comparative study. Journal of Field Robotics, 38(4), 532–550.
20. Huang, Y., Thomson, S. J., & Hoffmann, W. C. (2013). Development and evaluation of a low-cost precision sprayer. Transactions of the ASABE, 56(4), 1343–1352.
21. Kim, J., & Lee, J. (2023). Optimal placement of recharging stations for agricultural drone swarms. IEEE Transactions on Vehicular Technology, 72(5), 6145–6157.
22. FAA. (2021). Unmanned Aircraft Systems (UAS) Traffic Management (UTM) Concept of Operations v2.0. Federal Aviation Administration.
23. McMahan, B., Moore, E., Ramage, D., et al. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54, 1273–1282.
24. Mekonnen, M. M., & Hoekstra, A. Y. (2016). Four billion people facing severe water scarcity. Science Advances, 2(2), e1500323.
25. Everett, M., & How, J. P. (2019). Formal verification of neural network controlled autonomous systems. Annual Reviews in Control, 48, 243–256.
26. Mehrabi, N., Morstatter, F., Saxena, N., et al. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
27. Chouldechova, A., & Roth, A. (2020). A snapshot of the frontiers of fairness in machine learning. Communications of the ACM, 63(5), 82–89.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
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.
