## 6 Results  
### Model Prediction and Evaluation  
Two preprocessing methods and two optimization algorithms were cross-combined to build nine models, the epoch of the neural network was set to 300, and the batch size was 32. The training results are shown in Table 3. It can be seen from Table 3 that the effect of using the GA to optimize the neural network was better without any preprocessing method. When PCA and LDA were used for the data dimensionality reduction, the accuracy of the neural network model using the PSO algorithm improved instead. Comparing the two preprocessing methods, the robustness of the database constructed by PCA was higher. Compared with LDA, the R\({}^{2}\) of the PCA was higher by 1.77%, the RMSE was reduced by 0.59%, and the MAE was reduced by 0.31%. When the PCA is compared with no preprocessing method, the R\({}^{2}\) was higher by 4.36%, the RMSE decreased by 1.366%, and the MAE decreased by 0.39%. The three models with the highest accuracy in the test set were selected for comparison, as shown in Figures 8-10. It can be seen from the figure that the test data points mainly concentrated between 64% and 80%. Because the training data in this moisture content interval were higher, the fitting effect of the three models was the best in this interval. In general, the moisture content predicted by PCA-PSO-BP was closer to the real moisture content, and the prediction effect was better than other models. Locally, when the moisture content was less than 64%, the moisture content predicted by PCA-PSO-BP deviated seriously from the real moisture content; the moisture content predicted by the PCA-GA-BP model was relatively stable; and LDA-PSO-BP predicted that the moisture content was always greater than the real moisture content. When the moisture content was greater than 64%, the moisture content predicted by PCA-PSO-BP was more concentrated, but the other models were more dispersed. When the cluster of moisture content data points in the test set was split at 64%, the prediction results of the three models on the test set are shown in Tables 4 and 5. It can be seen from the table that the indexes of PCA-PSO-BP were better than the other models when the moisture content was greater than 64%. The MAE of PCA-GA-BP was higher than that of PCA-PSO-BP when the moisture content was less than 64%. In this paper, an improved BP neural network model was constructed to combine PCA-GA-BP and PCA-PSO-BP and to predict the moisture content of the Yinghong No. 9 tea leaves. In order to further reflect the advantages of the improved BP neural network model, it was compared with partial least squares (PLS) and support vector regression (SVR). The prediction effect on the test set is shown in Table 6. It can be seen from the various indicators that the improved BP neural network model was more suitable for the task of detecting the moisture content of the Y \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Preprocessing**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Training Set**} & \multicolumn{3}{c}{**Test Set**} \\ \cline{3-8}  & & **R\({}^{2}\) (\%)** & **RMSE (\%)** & **MAE (\%)** & **R\({}^{2}\) (\%)** & **RMSE (\%)** & **MAE (\%)** \\ \hline \multirow{6}{*}{PCA} & GA-BP & 95.0586 & 1.2387 & 0.8677 & 94.0175 & 1.3967 & 0.9435 \\  & BP & 90.1135 & 1.7678 & 1.2290 & 89.6210 & 1.8458 & 1.2661 \\  & PSO-BP & 92.6810 & 1.5107 & 1.1058 & 91.9585 & 1.6070 & 1.1687 \\ \hline \multirow{6}{*}{PCA} & GA-BP & 96.7769 & 1.0307 & 0.7281 & 96.4092 & 1.1021 & 0.7658 \\  & BP & 95.9545 & 1.1429 & 0.7222 & 94.6632 & 1.3474 & 0.8353 \\  & PSO-BP & 97.7952 & 0.8373 & 0.5565 & 97.6152 & 0.9006 & 0.6005 \\ \hline \multirow{6}{*}{LDA} & GA-BP & 96.0620 & 1.2328 & 0.9283 & 94.4255 & 1.5837 & 1.1858 \\  & BP & 94.4418 & 1.1921 & 0.9231 & 93.6325 & 1.3112 & 1.0346 \\ \cline{1-1}  & PSO-BP & 96.2484 & 1.0149 & 0.8264 & 95.3036 & 1.1861 & 0.9263 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Predictions from different models. Figure 8: PCA-GA-BP model prediction scatter plot.