## 3 Results and Discussions
### Classification Results and Evaluation
#### 3.2.2 Performance Analysis of Feature Extraction Technique  
The feature extraction-based classification models were developed using the activations derived from the global pooling layer of the CNN. Again, the models were developed in two stages, first on the dataset of real images and then on the combined dataset. Since the feature vector obtained from _AlexNet_ had 9216 activations, it offered a wide range of features for classification. The important reason behind choosing _AlexNet_ over other state-of-the-art models was its small convolution kernel sizes and network architecture, which supported the extraction of fine-grain details in the images. The performance of the models trained through SVM and LDA classifiers were compared. The classification results on the test data have been recorded in Tables 4 and 5, containing the mean precision, recall, and \(F1\) scores for the five independent runs. The overall accuracy registered by LDA (GAN-LDA) and SVM classifiers (GAN-SVM) was 96.0%. In the training data, LDA performed slightly better than SVM (94.3% and 92.4%). As anticipated, the synthetic images enhanced the performance of both classifiers. The \(F1\)-score of the SVM model increased from 0.935 to 0.960, and that of the LDA model increased from 0.943 to 0.959. Tables 4 and 5 demonstrate that the GAN-based augmentation method can provide an excellent performance boost to different classifiers, especially when developed using a \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class Name**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**\(F1\)-Score**} \\ \cline{2-7}  & **TL** & **SVM-TL** & **TL** & **SVM-TL** & **TL** & **SVM-TL** \\ \hline \hline \multicolumn{1}{c}{Charlock} & 0.8537 & 0.9012 & 0.9333 & 0.9733 & 0.8975 & 0.9358 \\ \hline \hline \multicolumn{1}{c}{Fat Hen} & 0.9571 & 0.9722 & 0.8933 & 0.9333 & 0.9241 & 0.9523 \\ \hline \hline \multicolumn{1}{c}{Shepherd’s Purse} & 0.9589 & 0.9863 & 0.9333 & 0.9333 & 0.9459 & 0.9591 \\ \hline \hline \multicolumn{1}{c}{Cranesbill} & 0.9726 & 0.9740 & 0.9467 & 0.9600 & 0.9594 & 0.9669 \\ \hline \hline \multicolumn{1}{c}{ Maize} & 0.9351 & 0.9722 & 0.9600 & 1.0000 & 0.9474 & 0.9859 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of classification results of SVM on deep features of _AlexNet_. The SVM and GAN-SVM refer to the SVM models trained with the real image data, and combined real and artificial datasets, respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class Name**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**\(F1\)-Score**} \\ \cline{2-7}  & **TL** & **GAN-TL** & **TL** & **GAN-TL** & **TL** & **GAN-TL** \\ \hline \hline \multicolumn{1}{c}{Shepherd’s Purse} & 1.0000 & 1.0000 & 0.8801 & 0.9333 & 0.9362 & 0.9655 \\ \hline \hline \multicolumn{1}{c}{Cranesbill} & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\ \hline \hline \multicolumn{1}{c}{ Maize} & 1.0000 & 1.0000 & 0.9867 & 0.9867 & 0.9933 & 0.9933 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Class Name**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**\(F1\)-Score**} \\ \cline{2-7}  & **TL** & **LDA-TL** & **TL** & **LDA-TL** & **TL** & **LDA-TL** \\ \hline \hline \multicolumn{1}{c}{Charlock} & 0.9853 & 0.9857 & 0.8933 & 0.9200 & 0.9370 & 0.9517 \\ \hline \hline \multicolumn{1}{c}{Fat Hen} & 0.9324 & 0.9589 & 0.9200 & 0.9333 & 0.9261 & 0.9459 \\ \hline \hline \multicolumn{1}{c}{Shepherd’s Purse} & 0.9452 & 0.9474 & 0.9200 & 0.9600 & 0.9324 & 0.9537 \\ \hline \hline \multicolumn{1}{c}{Cranesbill} & 0.9242 & 0.9615 & 1.0000 & 1.0000 & 1.0000 & 0.9606 & 0.9804 \\ \hline \hline \multicolumn{1}{c}{ Maize} & 0.9367 & 0.9487 & 0.9867 & 0.9867 & 0.9611 & 0.9673 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of classification results of LDA on deep features of _AlexNet_.