UAV Swarms in Smart Agriculture: Experiences and Opportunities
Chengyi Qu, Jayson Boubin, Durbek Gafurov, Jianfeng Zhou, Noel Aloysius, Henry Nguyen, Prasad Calyam
Abstract
Smart agriculture benefits from unmanned aerial vehicles (UAV), and in-field sensors to collect data used to make responsible crop management decisions which sustainably increase yields. In addition, smart agriculture relies on machine learning algorithms, creative networking solutions, and edge and cloud computing resources to collect, transfer, and process agricultural data. UAV can carry a wide array of sensors, maneuver rapidly throughout the field, apply treatments for some crop health problems, and can be flown by software. UAV, however, have small batteries and limited carrying capacities which keep missions short. In this paper, we provide an overview of state-of-the-art UAV swarm technology for smart agriculture, and present experiences from real-world agricultural UAV swarm case studies. We describe how quick mapping of large areas such as crop fields necessitates multiple UAV missions, potentially using multiple UAV simultaneously as a swarm. We detail how swarms of UAV have added advantages over a single UAV deployment. They can coordinate to map areas in parallel, leverage multiple sensor types, target areas for close inspection, and diagnose and treat problems rapidly. UAV swarms come with additional implementation difficulties beyond single UAV. We list challenges to implementers in terms of Resource allocation, compute orchestration, multi-agent mission planning and swarm goal definition. We also describe recent advances in edge computing, machine learning, and autonomy in orchestration and resource management techniques for swarm deployments. Finally, we conclude with research opportunities that future work can address to improve swarm performance, scale, and adoption for smart agriculture.