# Research on Global Navigation Operations for Rotary Burying of Stubbles Based on Machine Vision
## 3 Results and Discussion  
### Tests of Start Route Extraction  
Images of outdoor fields with different lights in different scenes were collected to verify the effectiveness of the algorithm. Scene 2 has a different light intensity from scene 1, and scene 3 is a different field scene. Under the conditions of different backgrounds, the light intensity and color temperature are different, so, in this study, the trial-and-error method is used to obtain the appropriate threshold of 0.15-0.42 for the H component. As shown in Figure 19, because the Hough transform method is more concerned about the uniform covariance of the local feature points in detecting the line features, some of the regional points in the path become separate lines, and thus, the error of the path obtained by the Hough transform method is larger. Compared with the Hough transform, the least squares method can obtain incomplete linear equations. The least squares method is a direct global minimum derivation, which has better global properties, but it is also susceptible to the influence of error points. In this study, redundant points are first eliminated using the Hough matrix and then outliers (error points) are eliminated using the RANSAC algorithm. Finally, as can be seen in Figure 19, the path fitting of the Hough matrix and RANSAC can  
Figure 18: Aerial view of the experimental field at Huazhong Agricultural University. reflect the information of field boundaries more comprehensively, and the experimental results using different backgrounds show that the method can accurately obtain the starting routes in different backgrounds and has strong robustness. In different scenarios, the Hough transform continuously searches for peaks by means of a two-dimensional cumulative array created by slopes and intercepts and finally obtains the straight line with the highest number of co-linear points. The least squares method determines the optimal straight line equation by calculating the least square sum of the errors in the distances from the feature points to the straight line. Table 5 shows the equations and time consumed for fitting lines for different methods used for navigation path fitting. The average time to fit the navigation path is 0.31 s for the least squares method, 0.73 s for the traditional Hough transform, and 0.49 s for the Hough matrix and RANSAC method proposed in this paper, which saves 0.24 s compared to the traditional Hough transform. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Scenes** & \(k\) & \(b\) & **Time/s** \\ \hline \multirow{3}{*}{The least squares method} & 1 & 2.36 & \(-\)27.27 & 0.33 \\  & 2 & 2.08 & 12.92 & 0.28 \\  & 3 & 5.85 & \(-\)91.63 & 0.34 \\ \hline \multirow{3}{*}{Hough transform} & 1 & 2.17 & 0.12 & 0.81 \\  & 2 & 2.37 & \(-\)29.85 & 0.66 \\  & 3 & 5.27 & \(-\)50.67 & 0.73 \\ \hline \multirow{3}{*}{The Hough matrix and RANSAC method} & 1 & 1.88 & 46.43 & 0.46 \\  & 2 & 1.71 & 71.29 & 0.48 \\ \cline{1-1}  & 3 & 7.01 & \(-\)172.10 & 0.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of field tests with different scenarios. Figure 19: Identification results of starting route by three different methods. The experimental samples are 60 pictures with three different backgrounds as shown in Figure 19. Four parameters such as target pixel ratio, navigation path acquisition elapsed time, average navigation path acquisition elapsed time, and average recognition accuracy are calculated for different pictures. The recognition accuracy is defined based on the degree of agreement between the slope of the obtained straight line and the slope of the straight line under normal sunlight intensity. The target pixel ratio is the percentage of the number of pixels to the total number of image pixels. The statistical results of the method for 20 samples in each scene are summarized and shown in Table 6. The starting route extraction method takes less than 0.51 s to recognize different scenes, and the average recognition accuracy is above 90%.