## 3 Results and Analysis  
### Random Forest Regression Model  
According to the second chapter of this paper, the random forest regression model has a relatively important unknown parameter; that is, the number of regression trees in the forest, N_ESTIMATORS. In order to obtain the best effect of the model, we need to choose the size of the N_ESTIMATORS.  
The size of the tree N_ESTIMATORS is selected by drawing the learning curve. By observing the variation of the model's \(R^{2}\) with the N_ESTIMATORS, the overall size of the random forest is selected. We can see from Figure 7 that when the value of N_ESTIMATORS is greater than 50, \(R^{2}\) tends to be basically stable. The larger N_ESTIMATORS is, the more computation and memory will be needed, and the longer training time will be. When N_ESTIMATORS is equal to 50, \(R^{2}\) is in the front of the stable part and the calculation time is short. Therefore, the random forest model parameter in this paper is selected as 50.  
Based on the data of the three growth stages of rice, the models of dry weight (g), fresh weight (g) and plant height (cm) of rice are established respectively with the characteristic variables selected above. The data is divided into training set and test set in a ratio of 4 to 1. In order to facilitate the comparison of model effects, we used the same test set and training set. The training set is used to build the model, and the test set is used to test the effect of the model.  
After completing the establishment of the random forest regression model, the test set data are substituted into the model to obtain the corresponding prediction results. The predicted results are drawn into a line chart for fitting with the actual situation in the test set, as shown in the figure below. It can be seen from Figure 8 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 random forest model established in this section has certain guiding significance.  
In order to verify the effectiveness of the fractal dimension in predicting rice dry weight, fresh weight and plant height, the model is established by removing the fractal dimension during modeling, and the established model is compared with the model with the added fractal dimension; their \(R^{2}\) and \(MAPE\) are compared. If the fractal dimension  
Figure 8: Fitting renderings of the random forest model.  
Figure 7: Parametric learning curve of random forest.  
By comparing Tables 8-10, it can be seen that when random forest is used to build the prediction model, the prediction accuracy of the model will also change with the change of the growth stage. Both the dry weight prediction model and the fresh weight prediction model show a downward trend, but even in the third stage, the \(R^{2}\) and \(MAPE\) of the model are still good. However, the plant height prediction model showed an opposite trend. With the growth of rice, the accuracy of the plant height prediction model will increase continuously. In conclusion, although the growth stage will have a certain influence on the effect of the random forest model, the influence is not significant and is within the acceptable range. Therefore, the random forest model has a good effect on the prediction of dry weight, fresh weight and plant height of rice.