AI-NATIVE WIRELESS DIGITAL TWINS FOR PRECISION AGRICULTURE: EDGE INTELLIGENCE AND PREDICTIVE FARM OPERATIONS IN 6G ECOSYSTEMS

Abstract

The rapid evolution of sixth-generation (6G) wireless networks is enabling intelligent, autonomous, and data-driven agricultural systems through the integration of artificial intelligence (AI), edge computing, and wireless digital twin technologies. This paper proposes an AI-native wireless digital twin framework for precision agriculture that combines Internet of Things (IoT) sensors, unmanned aerial vehicles (UAVs), weather stations, and Enterprise Private 5G/6G communication infrastructure to create a real-time virtual representation of agricultural fields. The proposed framework employs edge intelligence to process heterogeneous sensor data with minimal latency, enabling continuous monitoring of crop health, soil moisture, environmental conditions, and equipment status. Predictive AI models analyze the synchronized digital twin to forecast crop growth, irrigation requirements, disease outbreaks, and yield performance while supporting timely farm management decisions. Unlike conventional cloudcentric agricultural systems, the proposed architecture reduces communication delays, improves resource utilization, enhances network reliability, and supports scalable autonomous farming operations. Furthermore, the integration of AI-native networking and edge-based analytics strengthens sustainability by optimizing water consumption, fertilizer application, and energy usage. The framework also provides a foundation for future intelligent agricultural ecosystems by enabling real-time decision support, predictive farm operations, and adaptive wireless resource management. The proposed approach demonstrates the potential of AI-native wireless digital twins to transform precision agriculture into a resilient, efficient, and sustainable component of nextgeneration 6G ecosystems.