Abstract:

merging smart agriculture is critical for optimizing crop quality and quantity. However, its realization faces significant challenges, particularly the lack of feasible communication infrastructure and poor wireless connectivity in rural areas. This paper presents a novel Unmanned Aerial Vehicle (UAV) assisted two-tier agriculture network architecture to address these issues, where UAVs act as in- termediaries between agriculture sensors and cloud servers. Our key innovation is a Large Language Mode (LLM)-based approach for context-aware semantic mapping, introducing an innovative Semantic Criticality Index (SCI) that dynamically assesses the importance of agricultural data. This novel SCI drives our formulation of the agricultural sensor data collection scheduling problem as an optimization problem to minimize energy use in sensors and UAVs, solved using a proposed Semantic-Guided Deep Q-Network (SG-DQN) algorithm that optimizes energy consumption and resource allocation based on semantic context. Simulations using public agricultural datasets show significant improvements over traditional methods in energy efficiency and data classification accuracy.