## 3 Results and Analysis
### SVR Model  
In this section, the rice characteristics selected above are taken as explanatory variables. The dry weight, fresh weight and plant height are explained variables. The sklearn package of Python is used to establish prediction models of rice dry weight, fresh weight and plant height based on support vector regression. The grid search method of 10-fold cross-validation method is used to find the optimal combination of these two parameters, so that the model can achieve the optimal prediction effect. The optimization interval is determined as C belonging to 0 to 150 and Gamma to 0 to 10, respectively. A more detailed search is conducted with a step size of 1. According to the grid search results, when C equals 100 and gamma equals 4, the model achieves the highest \(R^{2}\).  
Based on the selected kernel function and the optimal parameter combination, the SVR function in the Python sklearn package is used to build the model. The 'PW', 'SA', 'SA/pH (V) \(\times\) PW', 'IFD', 'SFD', 'G_g', 'f1' and 'LD1' of three growth stages are used as explanatory variables to establish a prediction model for fresh weight (g) and dry weight (g). 'PW', 'PH(V)', 'PH', 'SFD', 'G_g', 'f1', 'f2' and 'LD2' are used as explanatory variables to establish a prediction model of plant height (cm).  
The data need to be normalized first, and then divided into a training set and test set in a ratio of 4 to 1. After the establishment of the SVR model, the corresponding prediction results can be obtained by substituting the data as the test set into the model. The predicted results are drawn into a line chart for fitting with the actual situation of the data in the test set, as shown in Figure 9. It can be seen from the figure that the predicted results of the model are close to the actual situation, and the distribution of data is well fitted. The prediction ability of the model is relatively stable. Under certain conditions, the predicted value of the model established in this section has certain guiding significance.  
According to the above, the growth stage of rice will have an impact on the prediction performance of the model.  
By comparing Tables 11-13, it can be seen that with the growth of rice, the \(R^{2}\) of the support vector regression model will change greatly and \(MAPE\) will hold steady. With the growth of rice, the prediction effect of fresh weight and plant height models will become better and better, and the increase in both models is greater than 0.05 at the third stage, which has a better prediction effect. However, with the growth of rice, the prediction effect of the dry weight model shows a downward trend; the \(R^{2}\) of the second stage is only 0.6639, and even in the third stage, the \(R^{2}\) of the model is less than 0.8. Although the \(R^{2}\) of the dry weight model meets the application standard of the model, the effect is too poor compared with the model obtained by other algorithms. Therefore, support vector regression is not recommended to be used when establishing the dry weight prediction model of rice. However, support vector regression can achieve better results when establishing the prediction model of rice fresh weight and plant height.