## 4 Results and Discussion  
_Driverless Agricultural Machine System Testing and Application Analysis_  
This research aims to analyse the trajectory tracking effect of the designed agricultural machinery system in order to better verify its application effect in the operation process. At the same time, this study chooses the paddy field environment as the experimental scenario because factors such as muddy soil conditions, uneven ground, and the presence of water in the paddy field environment require the operation, stability, and traction of agricultural machinery. Moreover, the common rice working environment is mostly the paddy field environment, and choosing this environment, one can better analyse the performance and application adaptability of agricultural machinery. The experimental site for the paddy field environment was selected as an ordinary paddy field in Yexie Town, Songjiang District, Shanghai, with uneven ground. This farmland often suffers from the issue of traditional rice direct seeders getting stuck in the mud during operation, and there is a certain degree of error in the row spacing during sowing. Especially in the turning section, there is a significant deviation between the planning results and the actual results when the live broadcast machine achieves lifting and lowering while turning the steering wheel. This study builds an autopilot and operating system based on a rice planter, with an experimental Android user system as the management terminal system, and uses radio and Bluetooth to build a remote communication link. The technical parameters of the main controller in the system platform are a dual-core Cortex-A8@800 MHz processor, Linux operating system, and 128 MBDDR2 and 256 MBSLCNAND memory and storage and the Ethernet interface is 10/100 M adaptive. And the parameters of the navigation and sensor devices used in the experiment are shown in Table 3.  
The proposed method and system of this study are examined in two scenarios, a concrete floor and paddy field environment, and the lateral deviation and heading angle deviation of the starting position of the agricultural crop machine from the AB line are set to 0.5 m and 0\({}^{\circ}\), respectively, and the travelling speed is set to 0.8 m/s. The schematic diagram of the experimental site for agricultural machinery operation is shown in Figure 7.  
Among them, the tracking control algorithm refers to path tracking control based on an unmanned navigation positioning system, and adaptive control is the PD algorithm. The proposed algorithm is a fuzzy adaptive PD algorithm that combines the multi-deviation feedback nonlinear control method with the PD algorithm. The direction deviation weight of the proposed adaptive fuzzy control algorithm is 1, the curvature deviation weight is 3, and the speed deviation weight is 0.05; the proportional adjustment coefficient in the PD control algorithm is 1.2, and the differential adjustment coefficient is 2.0. The preview distance of the pure tracking algorithm is set to 2 m. This study utilises the kinematic model of agricultural machinery and MATLAB tools for the simulation analysis. The initial lateral deviation is set to 0.5 m, the vehicle speed is set to 1 m/s, and the wheelbase \begin{table}
\begin{tabular}{c c} \hline \multicolumn{2}{c}{**Navigation Device Parameters**} \\ \hline RTK plane accuracy & 2.5 cm \\ \hline Heading angle accuracy & 0.1\({}^{\circ}\) \\ \hline Speed measurement accuracy & 0.01 m/s \\ \hline Data output method & RS232 (Four routes) \\ \hline Data refresh rate & 10 Hz \\ \hline Serial band rate & 115,200 bps \\ \hline \multicolumn{2}{c}{Parameters of angle sensor equipment} \\ \hline Range & 0\({}^{\circ}\)\(\sim\)360\({}^{\circ}\) \\ \hline Accuracy & 0.1\({}^{\circ}\) \\ \hline Resolving power & 0.023\({}^{\circ}\) \\ \hline Output method and frequency & RS485 (Two routes), 200 Hz \\ \hline \end{tabular}
\end{table} 
Table 3: Parameters of navigation and angle sensors. Figure 7: Agricultural machinery operation experimental site.