Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms

Author:

Ming Rui1,Jiang Rui23ORCID,Luo Haibo1,Lai Taotao1,Guo Ente1,Zhou Zhiyan23ORCID

Affiliation:

1. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China

2. Guangdong Laboratory for Lingnan Modern Agriculture, College of Engineering, South China Agricultural University, Guangzhou 510642, China

3. Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence (GDKL-AAI), Guangzhou 510642, China

Abstract

Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms’ automation systems, agricultural UAVs have been widely adopted across various operational stages due to their precision, high efficiency, environmental sustainability, and simplicity of operation. However, present-day technological advancement levels and relevant policy regulations pose significant restrictions on UAVs in terms of payload and endurance, leading to diminished task efficiency when a single UAV is deployed over large areas. Accordingly, this paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade. An initial overview presents the current control methods for UAV swarms, incorporating a summary and analysis of the features, merits, and drawbacks of diverse control techniques. Subsequently, drawing from the four main stages of agricultural production (cultivation, planting, management, and harvesting), we evaluate the application of UAV swarms in each stage and provide an overview of the most advanced UAV swarm technologies utilized therein. Finally, we scrutinize and analyze the challenges and concerns associated with UAV swarm applications on unmanned farms and provide forward-looking insights into the future developmental trajectory of UAV swarm technology in unmanned farming, with the objective of bolstering swarm performance, scalability, and adoption rates in such settings.

Funder

National Natural Science Foundation of China

Science Foundation of Fujian Province of China

Ji’an Science and Technology Program

Open Project Program of Guangdong Provincial Key Laboratory of Agricultural Artificial Intelligence

Publisher

MDPI AG

Subject

Agronomy and Crop Science

Reference119 articles.

1. The Role of Agriculture in Development;Gollin;Am. Econ. Rev.,2022

2. Islam, N., Rashid, M.M., Pasandideh, F., Ray, B., Moore, S., and Kadel, R. (2021). A Review of Applications and Communication Technologies for Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) Based Sustainable Smart Farming. Sustainability, 13.

3. Unmanned farm is one of the ways to realize digital agriculture;Luo;Digit. Front.,2021

4. Research on Evolutionary Impetus and Path of Unmanned Farm;Zhang;Shandong Agric. Sci.,2020

5. Cognitive WSN Control Optimization for Unmanned Farms Under the Two-Layer Game;Wu;IEEE Sens. J.,2022

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