## 3 Results and Analysis
### RBF-SVM Intelligent Grading Test Analysis  
The software was run on a test platform (Figure 9). The platform positions the charge-coupled device (CCD) industrial camera to a position where the best pictures can be taken, and the light emissivity controller is used to set the brightness of the light source. When a  
Figure 8: Yuba quality grading software interface.  
Figure 7: Comparison of prediction results and absolute percentage errors of three models. (**a**) Model prediction comparison; (**b**) comparison of model prediction errors.  
stable brightness is reached, the image is captured, and the photos are uploaded to the host computer through a USB connection. After opening the Yuba skin quality classification software, the Yuba skin images that need to be classified are automatically selected by clicking the image selection button, and the images are preprocessed and displayed. Then, in the feature value extraction module, the corresponding feature values are displayed in the software by clicking the corresponding button. Finally, the corresponding results of the quality classification can be attained by clicking the RBF-SVM classification button in the final quality result module.  
The collected multi-eigenvalue factors were input into the pre-established SVM grading model as input values, and the SVM grading models with different kernel functions were used to determine Yuba skin quality. Different grading results were obtained with different sum function models, and the specific grading test results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{4}{c}{**SVM Kernel Function**} \\ \cline{2-4}
**Eigenvalue Combination Method** & **LKF Linear** & **PKF Polynomial** & **RBF Radial** \\  & **Kernel** & **Kernel** & **Basis Core** \\ \hline ASM & 62.36\% & 67.62\% & 68.89\% \\ CON & 68.38\% & 72.06\% & 70.32\% \\ COR & 72.55\% & 74.65\% & 60.24\% \\ IDM & 78.54\% & 75.36\% & 67.75\% \\ SRE & 64.32\% & 73.68\% & 75.66\% \\ LRE & 73.59\% & 79.69\% & 69.98\% \\ GLD & 69.72\% & 82.69\% & 70.63\% \\ RLD & 72.28\% & 81.63\% & 72.39\% \\ DOD & 83.57\% & 82.77\% & 74.68\% \\ ASM+CON & 77.68\% & 83.64\% & 79.68\% \\ ASM+CON+COR & 82.32\% & 84.66\% & 85.55\% \\ ASM+CON+COR+IDM & 83.54\% & 85.78\% & 87.07\% \\ ASM+CON+COR+IDM+SRE & 84.36\% & 86.69\% & 88.46\% \\ ASM+CON+COR+IDM+SRE+LRE & 86.65\% & 87.68\% & 89.97\% \\ ASM+CON+COR+IDM+SRE+LRE+GLD & 86.64\% & 88.55\% & 90.54\% \\ ASM+CON+COR+IDM+SRE+LRE+GLD+RLD & 87.32\% & 88.87\% & 91.35\% \\ ASM+CON+COR+IDM+SRE+LRE+GLD+RLD+DOD & 88.87\% & 89.75\% & 93.56\% \\ \hline \end{tabular}
\end{table}
Table 4: Test results of different SVM kernel functions.  
Figure 9: Quality grading system test platform. (**1**) CCD; (**2**) Z stage; (**3**) LCD; (**4**) LED light source; (**5**) plane light source; (**6**) Yuba skin; (**7**) portable computer.  
The test results indicate that the SVM grading system's accuracy increased gradually with longer combinations of eigenvalues. The RBF radial basis kernel function had a 93.56% accuracy rate for the eigenvalue combination of ASM+CON+COR+IDM+SRE+LRE+GLD+RLD+DOD. Despite this promising result, further improvements can be made, possibly with a larger or more varied image dataset for training.