# Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis
## 4 Numerical Results  
The study performed the classification task on a PC equipped with an Intel Core i5-9400 processor, an NVIDIA 360 GPU with 4 GB of VRAM, and 8 GB of RAM. Before training, the data is automatically partitioned, and randomly selected images are placed into training, validation, and testing folders based on the specified ratios. After training and validation, the model is tested on images from the test folder. The model's performance parameters are evaluated using Equations (6)-(10). All results are presented in Table 3.  
The model achieved a rather high overall accuracy of 0.92, which indicates that the model is generally performing well across all classes. The macro averages indicate a precision of 0.94, a recall of 0.87, and an F1 score of 0.87. It can be inferred from the figures that the precision is generally high, despite the model may struggle with recall across certain classes, The weighted average, which accounts for the support of each class according to its incidence, indicates that the model's overall performance is slightly better in terms of precision (0.93), recall (0.92), and F1 score (0.90).  
The classification model performs well overall, especially in identifying the \"CATER-PILLAR\", \"HEALTHY\", \"MITE\", and \"WORM\" classes. The \"APHID\" class shows significant room for improvement, as indicated by its low precision, recall, and F1 score. While the model's high accuracy is promising, specific classes need targeted adjustments or more training data to enhance their detection capabilities.  
Figure 7a,b show the model's training and validation performance progress over 50 epochs. The model's training process is represented through (a) accuracy and (b) loss. The blue dotted line represents the model's training accuracy, and the orange dotted line indicates validation accuracy. Training accuracy starts at 0.2 and increases as the epochs progress, reaching approximately 0.6. The fact that validation accuracy is higher than training accuracy indicates that the model has a strong generalisation ability. However, after the 20 th epoch, there is no further improvement in validation accuracy, suggesting that the model has reached the maximum level of learning it can achieve from the dataset and will not show any more progress.  
In the second graph, the dotted blue line shows the model's training loss, while the dotted orange line represents validation loss. Training loss decreases continuously as the epochs progress, and validation loss is stabilised around the 20 th epoch. After this point, further training may result in overfitting. Overfitting occurs when the model becomes too closely adapted to the training data, reducing its generalisation ability. Therefore, training  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Class** & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline APHID & 1.00 & 0.23 & 0.38 & 13 \\ BURNT & 0.86 & 0.97 & 0.91 & 33 \\ CATERPILLAR & 1.00 & 1.00 & 1.00 & 15 \\ HEALTHY & 0.94 & 1.00 & 0.97 & 31 \\ MILDEW & 0.83 & 1.00 & 0.91 & 29 \\ MITE & 0.96 & 1.00 & 0.98 & 22 \\ WORM & 1.00 & 0.89 & 0.94 & 27 \\
**Accuracy** & & & 0.92 & 170 \\
**Macro Avg** & 0.94 & 0.87 & 0.87 & 170 \\
**Weighted Avg** & 0.93 & 0.92 & 0.90 & 170 \\ \hline \end{tabular}
\end{table}
Table 3: Summary of numerical results  
Figure 7: (**a**) Accuracy and loss (**b**) functions during the training process.  
should be stopped around the 20 th epoch, or early stopping techniques should be applied to prevent the model from overtraining.  
The test results are summarised in the confusion matrix presented in Figure 8. The image analysis in the test folder revealed that the learning process yielded foolproof outcomes for some specific classes, such as HEALTHY, CATERPILLAR, MILDEW, and MITE. Minor classification errors are obtained for BURNT and WORM classes, while misclassifications in the APHID class are more challenging. Although APHID predictions are mostly confused with MILDEW, there is some minor confusion between the BURNT and HEALTHY classes.