## 3 Results
### Analysis of Classification Results
#### 3.2.2 Classification Results for Different Models  
In this study, color, shape, and texture features were extracted for spore images. The classification models for LR, KNN, RF, and SVM were established. The accuracy rate, recall rate, and comprehensive evaluation index values for the three kinds of spore classifications using different classification models were compared and analyzed and the overall recognition ability of the classifier was summarized. The results of the comparative analysis are shown in Tables 3 and 4.  
From Tables 3 and 4, it can be seen that the SVM model has better overall classification performance. The average accuracy rate for the spore recognition using the SVM model was 94.36%, while the accuracy rates of the LR, KNN, and RF models were 90.13%, 89.37%, and 89.23%, respectively.  
Regarding the recognition of the three types of spores, the SVM model had higher accuracy, recall, and comprehensive evaluation indicators than the three other classification models. Regarding the comparison of the accuracy rates, the accuracy rate of the SVM model for class 1 spores was 87.30%,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Indexes**} & \multicolumn{6}{c}{**Classification Model**} \\ \cline{2-10}  & & **LR** & **KNN** & **RF** & **SVM** \\ \hline Accuracy (\%) & 90.13 & 89.37 & 89.23 & 94.36 \\ Precision (\%) & 89.51 & 87.64 & 89.64 & 91.76 \\ Recall (\%) & 90.02 & 88.65 & 85.92 & 91.67 \\ F1-Score (\%) & 89.65 & 88.09 & 87.72 & 91.68 \\ Recognition time (s) & 1.1241 & 1.6473 & 1.8597 & 1.5346 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall average recognition ability comparisons of different classification models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample Class**} & \multicolumn{4}{c}{**Basic Indicators**} \\ \cline{2-5}  & **TP** & **TN** & **FP** & **FN** \\ \hline
1 & 55 & 110 & 8 & 5 \\
2 & 58 & 107 & 1 & 2 \\
3 & 52 & 113 & 6 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification results for the SVM.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{6}{c}{**Classification Results**} \\ \cline{2-10}  & \multicolumn{3}{c}{**Precision**} & \multicolumn{3}{c}{**Recall**} & \multicolumn{3}{c}{**F1-Score**} \\ \cline{2-10}  & **LR** & **KNN**  
which was lower than the 89.26% achieved by the LR model and the 88.57% achieved by the RF model. The accuracy rates of SVM model for class 2 was 98.31%, which was lower than that of the LR model (98.21%). However, regarding the comparison of the accuracy rates, the SVM model as a whole reached 91.76%, which was better than the other three classification models.  
As a comprehensive evaluation index, the F1-score represents the overall performance of the classifier. Regarding the recognition of the three categories, the F value of the SVM model was higher and the overall average value reached 91.68%, which was 2.03, 3.59, and 3.96 percentage points higher than LR model, KNN model, and RF model, respectively, showing that the SVM model performed well.  
As we can see from Table 4, the recognition times for the LR model, KNN model, RF model, and SVM model were 1.1241 s, 1.6473 s, 1.8597 s, and 1.5346 s, respectively. The recognition time for the SVM model was higher than that of the LR model, however it was lower than the KNN model and RF model. The recognition time for spores for the classification model was much lower than the artificial recognition time [21]. If the dataset of the spore images is large, in order to improve the recognition speed, the work station can be used in real-world applications [28].