## 2 Methodological Background  
In smart agriculture, the IoT cloud platform integrates various sensors to enable real-time collection of environmental and operational data, facilitating intelligent decision-making for optimizing agricultural machinery operations. The literature [5] proposes a privacy-preserving data aggregation scheme based on the ElGamal encryption system to enhance the security and flexibility of data sharing in agricultural IoT. The literature [6] introduces the PPFchain framework, which enhances security, data privacy, and system performance in sensor-IoT architectures. To manage soil nitrogen levels more effectively, the literature [7] proposes an IoT sensing platform based on LoRaWAN. This system enables real-time monitoring of soil nitrate levels, optimizing fertilization management, improving nitrogen utilization efficiency, and reducing environmental pollution. These studies underscore the significance of IoT technology in data security, agricultural automation, and precision farming. The application of edge computing technology has significantly enhanced real-time data processing capabilities and improved the autonomous decision-making of agricultural machinery. For instance, the literature [8] proposes a smart farm information processing platform approach that emphasizes integrating diverse data sources to ensure interoperability, reliability, scalability, real-time data processing, and security within smart farming ecosystems. The literature [9] introduces a metaverse-enabled framework for open-terrain agricultural vehicle fleets, leveraging parallel intelligence and edge computing to enhance the operational efficiency and energy effectiveness of agricultural machinery. Additionally, the literature [10] develops a subsoiling monitoring system based on edge computing using IoT architecture, enabling real-time monitoring and analysis of soil conditions to optimize subsoiling operations and improve soil health. Furthermore, artificial intelligence (AI) and machine learning (ML) are increasingly being adopted in precision agriculture. By analyzing vast amounts of data collected by IoT sensors, these technologies optimize decision-making processes for agricultural machinery, enhance environmental perception accuracy, and refine path planning algorithms, thereby advancing the intelligence level of agricultural production [11,12]. However, the further development of smart agriculture relies not only on advanced AI algorithms but also on efficient and stable communication technologies to ensure real-time data transmission and coordinated operation of agricultural equipment [13]. Therefore, selecting the appropriate communication technology is crucial for the stable operation of agricultural IoT systems. A reliable and efficient communication network is essential for seamless data collection, remote control, and multi-machine collaboration. Different application scenarios have varying communication requirements. Currently, the mainstream agricultural communication technologies include NB-IoT, 5G, LoRa, Wi-Fi, and Zigbee, each with distinct advantages and trade-offs in terms of coverage, power consumption, bandwidth, and latency. The literature [14] explores the application of IoT communication technologies in smart agriculture, analyzing challenges related to data transmission, coverage, and energy consumption, and highlighting future directions for the development of communication technologies. The literature [15] proposes an IoT-based smart greenhouse framework and discusses control strategies for sustainable agriculture, optimizing greenhouse environ mental parameters to increase crop yield and reduce resource consumption. In the field of remote agricultural communication, the literature [16] highlights the potential of 6G-IoT technologies in reducing greenhouse gas emissions through enhanced real-time monitoring and intelligent decision-making, offering a new perspective on sustainable smart agriculture.