# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 4 Results
### Improved SSA-SVR Training Model Results
Similarly, the closer the Best accuracy is to 1, the better the model parameter optimization, while close to 0 or negative values indicate poor training capabilities of the model. The training time is 0.49 s.  
In Figure 3b, the training plot of the unimproved SSA-SVR model is shown. Taking the average values, the correlation coefficient is 0.947, the coefficient of determination is 0.938, the root mean square error is 1.92, and the Best accuracy is \(-0.89\). The training time is 0.37 s. Compared to the improved SSA-SVR model, the unimproved model has significantly lower performance in all metrics except for the training time, which is relatively shorter. Subsequently, the probability density map is added, as shown in Figure 4. The horizontal axis represents the value range of the data, and the vertical axis represents the probability density of the data points in the corresponding value range, so as to compare the distribution of the data set. This shows that the model training results are in high agreement with the real data. In a homemade grain moisture meter, the voltage value obtained is determined by the resistance under a constant temperature. The corresponding moisture value is then found according to the corresponding national standard. Generally, within the moisture range of 9-20, there is a near-linear relationship between the grain's internal moisture and the logarithm of its resistance. By taking data with moisture between 10 and 30, we can explore the relationship between resistance, voltage, and moisture values under the SSA + SVR model. Figure 5a shows the RMSE chart [41], Figure 5b displays the three-dimensional scatter plot, and Figure 6 depicts a three-dimensional surface plot. Figure 4: Improved SSA-SVR probability density diagram. Figure 3: (**a**) Shows the improved SSA-SVR training model diagram; (**b**) shows the train model diagram for SSA-SVR. The MSE plot in Figure 5 compares the actual values with the model training values. By plotting the scatter diagram of actual values against predicted values, the accuracy of the model predictions can be visually assessed. If the predicted values are very close to the actual values, the scatter points will tightly cluster around the 45-degree line (which represents the ideal one-to-one correspondence line). The MSE plot helps in visually evaluating the error distribution and prediction accuracy of the model. It can reveal patterns of prediction bias or the impact of outliers in the data on the model's performance. Furthermore, as a performance metric, MSE quantifies the prediction errors of the model and provides guidance for optimizing model parameters and structure. The combined use of the 3D surface plot in Figures 5 and 6 provides rich information and a deep understanding of the model performance. In the scatter plot, each point represents an actual observation, where the position of the point indicates the values of voltage and resistance, while the color or size represents the actual moisture content. This allows observers to easily see the distribution of data points in three-dimensional space. The 3D surface plot illustrates the complex relationship between voltage, resistance, and moisture content. This three-dimensional visualization method can reveal the degree and patterns of influence of different input features (voltage and resistance here) on the target variable (moisture content). Through this plot, one can observe the trend of moisture content variation under different resistance and voltage conditions. By visualizing the impact of different features on the target variable, a method is provided for a deep understanding of the model behavior and data structure. This is valuable for evaluating the model's complex nonlinear fitting ability and revealing the inherent relationships in the data. By comparing  
Figure 5: (a) Shows the RMSE diagram; (b) way to improve the SSA + SVR 3D scatter plot. Figure 6: Improved SSA + SVR 3D surface map.