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

Sungjun Seo, Kooktae Lee

TL;DR

This work tackles the challenge of scalable, nonuniform spraying for pest, weed, and disease management in large farms by marrying Optimal Transport with multi-UAV coordination. It introduces Density-Driven Optimal Control (D$^2$OC), a decentralized framework that models drones as Linear Time-Varying systems and uses Wasserstein-distance-based objectives to prioritize high-need areas while balancing workload. The method operates in three stages—Optimal Control, Weight Update, and Weight Sharing—to enable real-time, communication-aware coordination, with a closed-form solution for the LTV case. Simulations against Lawn Mower and Spectral Multi-scale Coverage demonstrate that D$^2$OC reduces chemical use and weed survivors more effectively, highlighting its potential for energy-efficient, large-scale smart agriculture.

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.

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

TL;DR

This work tackles the challenge of scalable, nonuniform spraying for pest, weed, and disease management in large farms by marrying Optimal Transport with multi-UAV coordination. It introduces Density-Driven Optimal Control (DOC), a decentralized framework that models drones as Linear Time-Varying systems and uses Wasserstein-distance-based objectives to prioritize high-need areas while balancing workload. The method operates in three stages—Optimal Control, Weight Update, and Weight Sharing—to enable real-time, communication-aware coordination, with a closed-form solution for the LTV case. Simulations against Lawn Mower and Spectral Multi-scale Coverage demonstrate that DOC reduces chemical use and weed survivors more effectively, highlighting its potential for energy-efficient, large-scale smart agriculture.

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.

Paper Structure

This paper contains 16 sections, 2 theorems, 23 equations, 12 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature Survey
  3. Contributions
  4. Problem Statement
  5. Problem Description
  6. Preliminary: Optimal Transport
  7. Problem Solving Approach
  8. Dynamics of the Spraying Drone
  9. Density-Driven Optimal Control (D$^2$OC)
  10. Stage A. Optimal Control Stage
  11. Stage B. Weight Update Stage
  12. Stage C. Weight Sharing Stage
  13. Simulations - Smart Agriculture for Weed Control
  14. Simulation Setup
  15. Performance Comparison between the LM, SMC, and D$^2$OC Methods
  16. ...and 1 more sections

Key Result

Proposition 1

Consider a spraying drone system (Fig. fig: QR_Spraying) obeying Assumption assumption. This system has time-varying parameters for the height of the solution and the mass as in eqn: m^k. Then, the moments of inertia of the system about $x'$-, $y'$-, and $z'$-axes, denoted by $I_{x'}^{k}$, $I_{y'}^{ where $I_{d,x}$, $I_{d,y}$, and $I_{d,z}$ are the moments of inertia of the drone about the $x$, $y

Figures (12)

  • Figure 1: Schematic of the proposed density-driven optimal control with the sample-point representation of the reference distribution.
  • Figure 2: Representation of the inertial frame and the body frame of the drone.
  • Figure 3: Illustration of the notations related to the solution and the spray tank.
  • Figure 4: Moments of inertia of the solution in the spray tank.
  • Figure 5: Stage A - prediction of the local sample-points over the finite prediction horizon $T$ = 3. The set of predicted local sample-points at time $k+i$ when the current time is $k$ is denoted by $S^{k+i|k}$, and $y^{k+i|k}$ is the mass center of the local sample-points contained in the $S^{k+i|k}$.
  • ...and 7 more figures

Theorems & Definitions (6)

  • Proposition 1
  • proof
  • Remark 1
  • Remark 2
  • Theorem 1
  • proof