AgriNex: Next-Gen Smart Agriculture with LLM-Integrated UAV-IoT Solutions

Emerging 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 (U...

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Vydané v:IEEE International Conference on Communications workshops s. 646 - 652
Hlavní autori: Hazarika, Ananya, Guo, Jianlin, Parsons, Kieran, Nagai, Yukimasa, Sumi, Takenori, Orlik, Philip, Rahmati, Mehdi
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 08.06.2025
Predmet:
Classification algorithms
Conferences
context-aware DQN problem-solving
context-aware semantic mapping
Data collection
Energy consumption
Energy efficiency
energy-efficient data collection scheduling
Intelligent sensors
LLM-based data labeling
Optimization
Semantics
Sensor systems
Smart agriculture
UAV-assisted smart agriculture
ISSN:2694-2941
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Shrnutí:Emerging 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 intermediaries 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.
ISSN:2694-2941
DOI:10.1109/ICCWorkshops67674.2025.11162136