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- Title
AI-Based UAV Swarms for Monitoring and Disease Identification of Brassica Plants Using Machine Learning: A Review.
- Authors
Ali, Zain Anwar; Dingnan Deng; Shaikh, Muhammad Kashif; Hasan, Raza; Khan, Muhammad Aamir
- 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 AIbased 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 detectionmethods are discussed. Investigations show the significance of using UAV swarms for growthmonitoring 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.
- Subjects
BRASSICA; DRONE aircraft; MACHINE learning; ARTIFICIAL intelligence; NUTRITION disorders
- Publication
Computer Systems Science & Engineering, 2024, Vol 48, Issue 1, p1
- ISSN
0267-6192
- Publication type
Article
- DOI
10.32604/csse.2023.041866