Multi-Agent Drone Swarms for Agricultural Intelligence Networks

Abstract

In modern precision agriculture, the use of Unmanned Aerial Vehicles (UAVs) has revolutionized the capabilities for real‑time monitoring of crop health, soil moisture, pest infestations, and resource allocation. However, single‑drone systems face inherent limitations in flight endurance, payload capacity, and operational redundancy, which constrain their effectiveness over expansive agricultural tracts. This study introduces and validates a multi‑agent drone swarm approach—comprising ten coordinated quadcopters—designed to function as an Agricultural Intelligence Network (AIN). Each drone is equipped with high‑resolution RGB and multispectral cameras, onboard processing units for in‑flight data analysis, and mesh‑network communications enabling peer‑to‑peer coordination without reliance on a central controller. We implemented a consensus‑based waypoint allocation algorithm alongside a U‑Net convolutional neural network for automated detection of pests and weeds. Field trials were conducted on a 50‑hectare maize plot characterized by heterogeneous health zones. Performance metrics included coverage rate (ha/min), detection accuracy (%), mission duration (min), and communication latency (ms). Statistical analysis using independent‑samples t‑tests (α = .05) across ten missions per configuration revealed that the swarm system achieved a 36% higher coverage rate and a 22.2% absolute improvement in detection accuracy compared to single‑drone missions, while reducing mission duration by 35%. Communication latency increased modestly but remained operationally acceptable. These results confirm that decentralized multi‑agent swarms significantly enhance both the efficiency and quality of agricultural surveillance. The paper discusses implications for large‑scale precision farming, addresses environmental and regulatory constraints, and outlines future research directions to further optimize swarm scalability and energy management.