Smart agriculture represents a core direction in modern agricultural development, aiming to enhance production efficiency and resource utilization through the integration of new-generation information technologies such as the Internet of Things, big data, and artificial intelligence. Among these, the deep integration of unmanned aerial vehicle technology and artificial intelligence is transforming traditional farmland management paradigms in unprecedented ways. This paper first introduces the basic concepts and current development status of smart agriculture, then elaborates on the dual roles of UAVs as low-altitude remote sensing platforms and intelligent operation equipment, focusing on key technologies for farmland information collection and precision variable-rate operations. The core of the paper lies in analyzing how deep learning-based object detection models are used for real-time analysis of UAV aerial images to achieve crop growth monitoring, pest and disease identification, and weed localization, thereby driving UAVs to execute decision-making for precise pesticide application and fertilization. Finally, the paper discusses in detail the technical characteristics of different object detection models and their applicability and selection basis in different application scenarios for agricultural UAVs.

Xia Shengzhe. Artificial Intelligence and UAVs in Smart Agriculture: Focusing on Information Collection and Precision Operations. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 11: 79-88. https://doi.org/10.25236/AJCIS.2025.081109.

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