Density-Driven Multi-Agent Coordination for Efficient Farm Coverage and Management in Smart Agriculture

2025-11-16

AI Summary

To address the challenges of precise pest and disease control, widespread pesticide overuse, and low collaborative coverage efficiency by multiple UAVs in large-scale farmland, this paper proposes a density-driven multi-UAV cooperative coverage framework. The method innovatively integrates optimal transport theory with multi-agent control to establish a non-uniform resource allocation mechanism guided by infection density, and formulates a linear time-varying dynamical model incorporating payload-induced mass variation. Leveraging Lagrangian mechanics, we derive a density-aware control law that enables dynamic load balancing and energy optimization. Simulation results demonstrate that, compared to uniform spraying and spectral multi-scale approaches, the proposed framework improves coverage efficiency by 23.6%, reduces pesticide consumption by 31.4%, and extends mission endurance by 18.9%. These gains significantly enhance the precision, sustainability, and scalability of agricultural aerial spraying.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The growing scale of modern farms has increased the need for efficient and adaptive multi-agent coverage strategies for pest, weed, and disease management. Traditional methods such as manual inspection and blanket pesticide spraying often lead to excessive chemical use, resource waste, and environmental impact. While unmanned aerial vehicles (UAVs) offer a promising platform for precision agriculture through targeted spraying and improved operational efficiency, existing UAV-based approaches remain limited by battery life, payload capacity, and scalability, especially in large fields where single-UAV or uniformly distributed spraying is insufficient. Although multi-UAV coordination has been explored, many current frameworks still assume uniform spraying and do not account for infestation severity, UAV dynamics, non-uniform resource allocation, or energy-efficient coordination. To address these limitations, this paper proposes a Density-Driven Optimal Control (D2OC) framework that integrates Optimal Transport (OT) theory with multi-UAV coverage control for large-scale agricultural spraying. The method supports non-uniform, priority-aware resource allocation based on infestation intensity, reducing unnecessary chemical application. UAVs are modeled as a linear time-varying (LTV) system to capture variations in mass and inertia during spraying missions. The D2OC control law, derived using Lagrangian mechanics, enables efficient coordination, balanced workload distribution, and improved mission duration. Simulation results demonstrate that the proposed approach outperforms uniform spraying and Spectral Multiscale Coverage (SMC) in coverage efficiency, chemical reduction, and operational sustainability, providing a scalable solution for smart agriculture.

Problem

• Addresses inefficient multi-UAV coordination for large-scale farm management
• Reduces excessive chemical use through priority-aware resource allocation
• Overcomes battery life and scalability limitations in agricultural spraying

Innovation

• Density-driven optimal control for multi-UAV coordination
• Optimal transport theory enables priority-aware resource allocation
• Linear time-varying system modeling improves workload distribution