## 4 Key Enabling Technologies
### Discussion  
Experimental results have validated the effectiveness of the proposed IoT-based cloud platform in addressing the communication and scheduling challenges of autonomous agricultural machinery, ensuring stable connectivity across diverse agricultural environments. Specifically, NB-IoT supports low-power, wide-coverage environmental sensor data collection, while 5G facilitates high-bandwidth, low-latency applications such as task scheduling and path tracking. In contrast, solely utilizing 4G, Wi-Fi, or NB-IoT presents certain limitations. For instance, 4G and Wi-Fi networks may suffer from insufficient coverage and latency issues in expansive agricultural settings, impacting real-time data transmission and machinery coordination [51]. On the other hand, while single-channel NB-IoT excels in low power consumption and wide coverage, it may not meet the demands of applications requiring high data transmission rates and low latency [52]. Therefore, integrating NB-IoT and 5G dual-channel communication significantly enhances data transmission reliability, thereby improving the responsiveness and efficiency of agricultural operations.  
Based on the Dijkstra algorithm and the IIHHO algorithm, the multi-machine cooperative scheduling method optimizes task allocation by minimizing idle time and operational costs. Gantt chart results indicate that the proposed scheduling strategy effectively balances the workload between primary and secondary machines, thereby improving field operation efficiency. However, uncertainties such as unexpected machinery failures and sudden weather changes in real agricultural environments necessitate further enhancement of dynamic scheduling adaptability, calling for the development of more robust algorithms capable of real-time adjustmentsunder variable conditions to maintain operational efficiency. Moreover, the multi-objective path-tracking predictive control method exhibits superior tracking accuracy compared to traditional control algorithms. The integration of the improved APO algorithm with MPC effectively reduces tracking error and enhances system stability, offering stronger performance in coping with complex environments compared to existing control methods.  
Despite the rapid configuration advantages of the proposed dual-channel communication autonomous agricultural machinery networking cloud platform, from the perspective of agricultural managers, its functions of automatic task assignment and autonomous operation simplify decision-making and reduce labor costs, thereby facilitating efficient management and operation. However, deploying and maintaining such a complex system in real agricultural environments still faces numerous practical challenges. Firstly, the initial investment is high, including the cost of sensors, automated agricultural machinery, and supporting infrastructure, which poses a considerable financial burden for many farmers, especially small and medium-sized operators. Secondly, the complexity and variability of agricultural environments, characterized by diverse terrain, vegetation, and climatic conditions, can affect the stability of communication and data transmission, thereby requiring the system to be highly adaptive and flexible. In addition, system integration and maintenance involve the coordination of multiple technologies, leading to high operational complexity, and users may face insufficient technical training and difficulties in troubleshooting. Furthermore, the deployment and maintenance of 5G base stations in expansive farmland also present challenges, as they require not only a reasonable layout to ensure signal coverage but also careful control of investment and operating costs.