AgriSwarm-RL: Multi-Agent Reinforcement Learning for Dynamic Task Allocation and Cooperative UAV Spraying in Heterogeneous Crop Fields (Journal of Advanced Artificial Intelligence Research, 2026)
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.
Keywords: multi-agent reinforcement learning, drone swarm, precision agriculture, task allocation, cooperative spraying, heterogeneous fields, sustainability, governance