## 2 Methodological Background
For instance, Simulated Annealing (SA) has been applied to optimize scheduling and path planning, reducing operational costs and enhancing adaptability to complex farmland environments [25]. The Multi-Site Vehicle Routing Problem (VRP) dynamically adjusts vehicle paths to generate efficient work schedules [26]. Mixed Integer Programming (MIP) is used to optimize crop harvesting and transportation routes, improving overall operational efficiency [27]. Deep Reinforcement Learning (DRL) enhances multi-machine task scheduling, increasing the intelligence of path planning [28]. Additionally, NSGA-III and improved Ant Colony Optimization algorithms are employed to predict unloading points and refine work paths, facilitating multi-machine collaboration [29]. Precise path tracking control algorithms are critical for ensuring the efficient and stable operation of agricultural machinery. As agricultural automation advances, research on path tracking algorithms has gained increasing importance. The literature [30] combines satellite and vision-based integrated navigation and employs a forward-looking control algorithm to achieve autonomous navigation of a tracked tractor in rice rows. The literature [31] proposes a model-free adaptive predictive control combined with PID (MFAPC-PID) for tractor path tracking. This method treats MFAPC as an adaptive integral term, which, together with the adaptive proportional and derivative terms, forms the MFAPC-PID controller. The literature [32] highlights the significant advantages of MPC in trajectory tracking. However, its high computational complexity and real-time performance limitations have driven researchers to explore various improvement methods to enhance the system's response speed and adaptability. While MPC offers significant advantages in trajectory tracking,  
Figure 3: (a,b) Performance comparison of the two communication technologies. its high computational complexity and limited real-time performance have prompted researchers to explore various enhancements. For example, Adaptive Parameterized MPC with Fuzzy Control dynamically adjusts parameters to improve system response [33], while Data-driven MPC (DD-MPC) eliminates the need for explicit models, increasing system robustness and adaptability [34]. Additionally, Particle Swarm Optimization-based real-time path planning algorithms refine obstacle avoidance, enhancing tracking accuracy [35]. I-DQN Deep Reinforcement Learning algorithms improve the environmental adaptability of agricultural robots [36], and IA* combined with Fuzzy Sliding Mode Control (F-SMC) optimizes path planning to increase agricultural machinery efficiency [37]. Despite the significant role of IoT, artificial intelligence, and advanced communication technologies in smart agriculture, several challenges remain. Reliable connectivity is still a critical factor affecting the coordination of agricultural machinery and data transmission in remote and complex environments. Additionally, the integration of multi-machine collaborative task scheduling with agricultural IoT requires further refinement to enhance operational efficiency, ensure optimal resource allocation, and maintain seamless task execution. Moreover, the performance of existing path-tracking control algorithms remains suboptimal in practical applications, requiring further improvements in stability and accuracy. Addressing these challenges will be essential for advancing smart agriculture toward greater efficiency, reliability, and intelligence.