# RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy
## 3 Experimental and Discussion
### Field Trials  
A few field trials were conducted in the Longkang Farms of Guoyang, Anhui Province, China in July and August, 2015, shown in Figure 12. In the field tests, six different lines were randomly selected as lanes, and the passage of the vehicle in the cornfield was detected through image recognition and path planning. The test line had a length of 100 m, and the crop rows, which were organized using the segmented measurement method, had a width of 60 to 80 cm. The vehicle autonomously navigated the field using the camera. The speed of the vehicle was set to 0.3-1.2 m/s, with a velocity increment of 0.3 m/s. The collision rate, the width of the row at the collision position, and the distance of the vehicle beyond the boundaries of the row were recorded during the testing of the vehicle through the test area. The average image processing time, including network latency, is 220 ms, which meets the maximum 2 m/s speed of the vehicle. In this system, due to the limitation of range capacity and power consumption of the system, images acquired are sent to the control platform wirelessly. The average image processing time includes network latency. The wireless bridge maximum power is 800 mW. Furthermore, the crop has a certain blocking effect on signal transmission, a delay time that cannot be ignored. By measuring in the corn field, transmission delay is about 100-150 ms on average for 100 m distance. The speed of processing satisfies the speed of the vehicle moving in the field. The majority of the maize was planted mechanically and the remainders were planted manually. Thus, the operating line in most of the area is almost a straight path, and some parts of the area are not straight. The passage of the vehicle through 100 m operating lines of four test columns was recorded, as well as its collision rates, the width of the crop row in collision, and the robot collision in the field beyond the distance of the boundary. \begin{table}
\begin{tabular}{c|c c c c|c c c c} \hline
**Velocity** & \multicolumn{4}{c|}{**RBF (\%)**} & \multicolumn{4}{c}{**Hough Transform (\%)**} \\ \hline
**m/s** & **Straight** & **Left Turn** & **Right Turn** & **S-Turn** & **Straight** & **Left Turn** & **Right Turn** & **S-Turn** \\ \hline
0.3 & 0.00 & 0.03 & 0.07 & 0.17 & 0.00 & 0.13 & 0.17 & 0.30 \\
0.5 & 0.00 & 0.10 & 0.07 & 0.20 & 0.00 & 0.17 & 0.13 & 0.33 \\
0.7 & 0.00 & 0.07 & 0.10 & 0.13 & 0.00 & 0.17 & 0.20 & 0.43 \\
0.9 & 0.03 & 0.13 &Figure 13 shows a histogram of the stability of the system in the case of a vehicle running at different speeds. In all cases, the stability of the system decreases with increasing speeds. When the speed is too fast, the response of the system cannot meet the needs of the vehicle, and the response time of the algorithm entails higher requirements. The experiments showed that the efficiency of the vehicle, road conditions (e.g., width of the workspace and weeds between the crops), and system responsiveness have positive effects. As shown in Table 3, four trials were conducted on the statistical data with a speed of 0.6 m/s. The collision rates were 5.04%, 6.38%, 5.65%, and 4.69%, and the average probability of a vehicle collision with stalks was 4.3%. The first group of data into Figure 13 was analyzed. Figure 12: Testing of fogging machine in the field.