Abstract— This collection of study explores advancements in drone technology across various domains, including military reconnaissance, agriculture, emergency communications, and forensic applications. By integrating AI and machine learning technologies such as YOLO models, reinforcement learning, and advanced neural networks, drones are achieving greater autonomy, precision, and adaptability. The research highlights the use of synthetic and augmented datasets for model training, the development of lightweight architectures for real-time applications, and innovative algorithms for resource-constrained environments. Challenges such as environmental complexity, ethical concerns, computational limitations, and communication reliability are analyzed, alongside solutions like simulation-to-reality transitions, adaptive algorithms, and robust evaluation frameworks. This paper offers a comprehensive examination of the evolving landscape of drone technology, emphasizing its potential for societal impact across diverse applications.
Keywords— Drone Technology, Artificial Intelligence, YOLO Object Detection, UAV Applications, Reinforcement Learning, Military Reconnaissance, Precision Agriculture, Emergency Communication Systems, UAV Forensics, Simulation-to-Reality Transition

| DOI: 10.17148/IARJSET.2024.111220