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AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review

Zain Anwar Ali1,2,*, Dingnan Deng1, Muhammad Kashif Shaikh3, Raza Hasan4, Muhammad Aamir Khan2

1 School of Physics and Electronic Engineering, Jiaying University, Meizhou, China
2 Electronic Engineering Department, Sir Syed University of Engineering & Technology, Karachi, Pakistan
3 Software Engineering Department, Sir Syed University of Engineering & Technology, Karachi, Pakistan
4 Department of Science and Engineering, Solent University, Southampton, SO140YN, UK

* Corresponding Author: Zain Anwar Ali. Email: email

Computer Systems Science and Engineering 2024, 48(1), 1-34. https://doi.org/10.32604/csse.2023.041866

Abstract

Technological advances in unmanned aerial vehicles (UAVs) pursued by artificial intelligence (AI) are improving remote sensing applications in smart agriculture. These are valuable tools for monitoring and disease identification of plants as they can collect data with no damage and effects on plants. However, their limited carrying and battery capacities restrict their performance in larger areas. Therefore, using multiple UAVs, especially in the form of a swarm is more significant for monitoring larger areas such as crop fields and forests. The diversity of research studies necessitates a literature review for more progress and contribution in the agricultural field. In this review, the comparative analysis of existing literature surveys is explored. This paper aims to provide an overview of AI-based UAV swarms, different cameras and sensors, image processing, and machine learning (ML) algorithms for image analysis having the purpose of monitoring and disease identification. Brassica plants are focused as they are grown on wider scales globally. Brassica species, the commonly infected diseases, and different types of disease detection methods are discussed. Investigations show the significance of using UAV swarms for growth monitoring growth for yield estimation, health monitoring, water status monitoring and irrigation management, nutrition disorders monitoring, pest and disease detection, and pesticide and fertilizer spraying in Brassica plants. Finally, some challenges of swarm-based applications are also addressed that require future consideration. The significance of this paper is that it suggests its readers embrace swarm-based technologies in the pursuit of more efficient production with relevant economic benefits.

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Cite This Article

Z. A. Ali, D. Deng, M. K. Shaikh, R. Hasan and M. A. Khan, "AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review," Computer Systems Science and Engineering, vol. 48, no.1, pp. 1–34, 2024.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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