## 3 Results and discussion
### Analysis of the Experiment Results in the Field
The response surface and contour map of the influence of the rotating speed weeding wheels and weeding depth on the inter row weeding rate are shown in Figure 10. When the rotating speed of the weeding wheels was constant, the inter row weeding rate first slowly increased and then slowly decreased with increasing weeding depth. When the weeding depth was constant, the inter row weeding rate increased with the increase in the rotating speed of the weeding wheels. The response surface changed quickly along the direction of the rotating speed of the weeding wheels and relatively slowly along the direction of the weeding depth, which indicated that the influence of the rotation speed of the weeding wheels on the inter row weeding rate was more significant than that of the weeding depth on the inter row weeding rate. Figure 10: Influence of the rotating speed of the weeding wheels and weeding depth on the inter row weeding rate: (a) response surface; (b) contour map. Figure 9: Influence of the forward speed and weeding depth on the inter row weeding rate: (a) response surface; (b) contour map. To improve the inter row weeding rate, the best parameter combinations for the forward speed, rotating speed of the weeding wheels and weeding depth were explored, and the experimental factors were optimized. The experimental factors and performance indexes should meet certain conditions. First, the inter row weeding rate should be maximized to meet the weeding requirements. Then, to ensure efficient weeding, the forward speed and rotating speed of the weeding wheels should be maximized. In addition, the weeding depth did not have a significant effect on the inter row weeding rate, and the rotating conditions could be taken as the mean value. The parameterized mathematical model was established as follows:  
\[\left\{ \begin{array}{c}\max y\\ \max x_{1}\\ \max x_{2}\\ 0.43\;\mathrm{m/s}\leq x_{1}\leq 0.77\;\mathrm{m/s}\\ 86.4\;\mathrm{r/min}\leq x_{2}\leq 153.6\;\mathrm{r/min}\\ 43.2\;\mathrm{mm}\leq x_{3}\leq 76.8\;\mathrm{mm}\\ 0\leq y(x_{1},x_{2},x_{3})\leq 1\end{array} \right. \tag{34}\]  
The multi-objective parameter optimization module of Design Expert 8.0 data analysis software was used to optimize the mathematical model, and the optimal combination of parameters was obtained as follows: the forward speed was 0.64 m/s, the rotating speed of the weeding wheels was 140 r/min, the weeding depth was 56.8 mm, and the inter row weeding rate of the model was predicted to be 88.43%. To verify the accuracy of the optimal design model, the forward speed was 0.64 m/s, the rotating speed of the weeding wheels was 140 r/min, the weeding depth was 56.8 mm, and the inter row weeding rate was set as the verification index. The validation experiment was repeated five times, and the average value was taken. The quantic inter row weeding rates obtained from the field verification experiments were 86.23%, 85.41%, 89.60%, 87.19% and 86.85%. The average inter row weeding rate of the quantic experiments was 87.06%. The experimental results were basically consistent with the optimization results, the relative error was 1.5%, and the error was within an acceptable range. The complex paddy field environment, the forward vibration of the machine, the straightness of the driving path and other factors that can cause a change in the weeding effect may account for the relative error. However, with the increase in repeated experiments, the actual experimental results will be closer to the model prediction results. The inter row weeding rate generally met the agronomic requirements. This paper mainly studies the mechanical inter row weeding parts, compared with chemical spraying weeding, mechanical weeding has become a key measure for the production of organic rice because of its advantages of not polluting the environment, increasing soil aeration, and improving the soil temperature.