A systematic review of UAV and AI integration for targeted disease detection, weed management, and pest control in precision agriculture

Abstract

Unmanned aerial vehicles (UAV), often unmanned aerial systems, are increasingly used in many industries, such as agriculture, forestry, the military, and disaster management. This is because they have the potential to perform tasks remotely without human intervention. This study comprehensively analyzes the latest developments in UAV technology for crop disease detection, weed management, and pest control. The focus of this study is on the incorporation of machine learning and deep learning algorithms into these UAV systems. We have conducted a thorough analysis of recent studies, particularly 2022–24, to evaluate the effectiveness of different unmanned aerial vehicle models, sensor types, and computational methods to improve crop monitoring and disease control strategies. This study highlights the remarkable agricultural production and sustainability improvements that UAVs enable. These vehicles provide accurate and practical information on crop health and the presence of weeds, detecting diseases and controlling pests, leading to valuable insights. However, obstacles remain in terms of data management, algorithmic complexity, and operational constraints under different environmental conditions. We discuss potential solutions and areas for future research to address current shortcomings and stimulate further improvements in agricultural operations using unmanned aerial vehicles. This in-depth exploration highlights the significant opportunities that unmanned aerial vehicles offer in agriculture and draws attention to critical areas where innovation and research are still needed.

Keywords

• Unmanned aerial vehicles
• Artificial intelligence
• Precision agriculture
• Disease detection
• Weed management
• Pest control