# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 4 Conclusions  
In this study, acquiring sufficient E1 waveforms from the citrus psyllid EPG signal collection proved challenging, leading to a limited quantity of E1 waveforms and an uneven distribution of datasets, which ultimately affected the modelling recognition performance. Consequently, a data augmentation method based on DCGAN was employed to generate EPG data of E1, aiming to optimize sample data and enhance the recognition performance. Additionally, to assess the reliability of the EPG data generated by DCGAN and quantitatively evaluate the error between the generated and real data, reliable waveforms were selected based on visual discrimination results, SVD plots, and MMD evaluation metrics. Four classification models (CNN, KNN, DT, and SVM) were established to evaluate the performance of DCGAN-based data augmentation and identify the seven types of EPG signals. The experimental results demonstrated that the proposed data augmentation method could generate reliable samples for imbalanced datasets, thereby improving the modelling performance. Furthermore, continuously adding samples to the training set \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Mode** & **Index** & **1 (A)** & **2 (B)** & **3 (C)** & **4 (PD)** & **5 (E1)** & **6 (E2)** & **7 (G)** \\ \hline \multirow{4}{*}{CNN} & Precision & 0.9573 & 1.0000 & 0.9777 & 1.0000 & 1.0000 & 0.9423 & 1.0000 \\  & Recall & 0.9438 & 1.0000 & 0.9441 & 1.0000 & 1.0000 & 0.9457 & 1.0000 \\  & F1 Score & 0.9505 & 1.0000 & 0.9606 & 1.0000 & 1.0000 & 0.9440 & 1.0000 \\ \hline \multirow{4}{*}{KNN} & Precision & 0.70199 & 0.96193 & 0.78922 & 0.96392 & 0.99404 & 0.72532 & 0.8623 \\  & Recall & 0.72674 & 1.0000 & 0.60626 & 1.0000 & 1.0000 & 0.84196 & 0.81450 \\  & F1 Score & 0.71415 & 0.9806 & 0.68574 & 0.98163 & 0.99701 & 0.7793 & 0.83772 \\ \hline \multirow{4}{*}{SVM} & Precision & 0.73554 & 0.91304 & 0.90215 & 0.85374 & 0.96899 & 0.65401 & 0.84105 \\  & Recall & 0.69736 & 0.89189 & 0.42410 & 0.95722 & 1.0000 & 0.97135 & 0.89126 \\  & F1 Score & 0.71594 & 0.90234 & 0.57697 & 0.90252 & 0.98425 & 0.78170 & 0.86542 \\ \hline \multirow{4}{*}{DT} & Precision & 0.82014 & 0.98292 & 0.82222 & 0.99645 & 0.99900 & 0.71622 & 0.80843 \\  & Recall & 0.71009 & 1.0000 & 0.68077 & 1.0000 & 1.00000 & 0.88170 & 0.89979 \\ \cline{1-1}  & F1 Score & 0.76115 & 0.99139 & 0.74484 & 0.99822 & 0.9995 & 0.79039 & 0.85166 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of model performances in identifying different types of EPG signals. further improved the classifiers discrimination accuracy. The CNN learned more EPG data features from the incremental data, leading to a significant improvement in classification accuracy on the test set. However, it is still challenging to integrate time-series analysis to further enhance classification accuracy and improve the visualization of classification results to clarify relationships between different waveforms. The DCGAN-CNN model based on reliable evaluation metrics could effectively mitigate dataset imbalance or limitations in nucleus recognition, thus significantly improving the classification accuracy of EPG signals. Even when classifiers lacked sufficient data fitting and classification capabilities, such as the SVM and DT models, the improvement in classification accuracy with data augmentation still showed efficacy. This study provides methodological guidance and data support for further exploration of the probing behavior and transmission mechanisms of citrus psyllids. Conceptualization, X.K., C.W. and L.Z. ; Methodology, X.K., C.W., L.Z. and F.Q. ; Software, C.W., W.Z., N.H. and S.C.; Validation, X.K., C.W. and T.Z. ; Formal Analysis, X.K., C.W. and F.Q. ; Investigation, X.K., C.W., W.Z. and L.Z. Data Curation, X.K., C.W. and F.Q. ; Writing--Original Draft Preparation, X.K., C.W. and F.Q. ; Writing--Review and Editing, X.K., C.W. and F.Q. ; Visualization, X.K., L.Z. and S.C.; Supervision, H.W., X.K., T.Z. and F.Q. ; Project Administration, L.Z. and F.Q. ; Funding Acquisition, T.Z. and F.Q.