## 4 Results and Discussion
obstacles due to the need to adjust the angle. The deviation situation of the two methods during farmland operation was analysed, and the results are shown in Figure 7:2  
The results in Figure 14 indicate that the proposed fuzzy control method has a smaller lateral deviation during operation than the adjacent method, and its deviation values on different bands are all at (\(-\)0.05,0), with an average lateral deviation value of \(-\)0.034. The lateral deviation results under the adaptation method vary significantly, especially near obstacles, with an overall average lateral deviation value of 0.042. The above results indicate that the proposed fuzzy control method exhibits good deviation adaptability and stable processing performance in job planning. The path tracking deviation results of different operation sections were analysed and the results are shown in Figure 7:3  
The results in Table 4 show that the root mean square (RMS) and average absolute values of the lateral deviation for each path sequence do not exceed 0.042 m and 0.029 m, the average absolute error and RMS values of the overall heading angle results are 0.017 m and 1.88\({}^{\circ}\), respectively, and more than 95 per cent of the segments have lateral deviation results that are within 0.11 m. The application evaluation of the two main agricultural environments under the fuzzy adaptive control algorithm used in this study is shown in Figure 7:4  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **Path** \\ **Number** \\ \end{tabular}  } & \multicolumn{6}{c}{**Lateral Deviation/m**} & \multicolumn{6}{c}{**Heading Angle Deviation/\({}^{\circ}\)**} \\ \cline{2-9}  & **Maximum** & **Minimum** & **Average** & **Standard** & **Maximum** & **Minimum** & **Average** & **Absolute** & **Standard** \\  & **Value** & **Value** & **Value** & **Deviation** & **Value** & **value** & **Value** & **Value** & **Deviation** \\ \hline \(1\) & 0.076 & \(-\)0.066 & 0.041 & 0.029 & 7.821 & \(-\)4.941 & 0.028 & 2.133 \\ \hline \(2\) & 0.102 & \(-\)0.075 & 0.024 & 0. The work zone (or work area/work range) refers to the predetermined flight path or work area for unmanned aerial vehicles to perform agricultural operations. It is often divided based on the shape of the farmland and the surrounding environment. The selection and planning of the work zone are crucial for improving work efficiency and coverage. The results of Figure 15 indicate that when using the proposed fuzzy adaptive control algorithm to analyse the results of agricultural machinery operation planning, its tracking accuracy for natural and artificial agricultural environments is mainly above 70%, and the average clustering value is mainly distributed between 85% and 95%, which has good application adaptability. Subsequently, a farmland experimental environment was selected for further research on path control, and tracking experiments were conducted on agricultural machinery under different road conditions in the \"S\"-shaped experimental path. The instantaneous control error was recorded, and a forward distance of 0.5 m was set. The proposed method was compared with the efficiency-oriented path tracking control algorithm (EfiMPC) [35], the MPC path tracking control method based on agricultural machinery attitude (MPC) [5], and the improved A * algorithm combined with the fuzzy sliding mode variable structure control method (IA *--F-SMC) [36]. The tracking performance of the different algorithms under the control path of straight and curved segments is discussed separately. Figure 16 shows the control error results of the different tracking methods under a straight-line segment. The results in Figure 16 indicate that the proposed method can effectively complete the tracking task of the entire path on the straight-line segment, and the motion trajectories are relatively close to the target path, with a direction roughly the same as the target path. Figure 16: Control errors of different tracking methods under straight-line segments.