# Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis
## 5 Discussion  
The classification of various diseases affecting pepper leaves is based on their distinct visual and colour differences, as an important guide to ensure accurate differentiation. Healthy pepper leaves are predominantly uniformly bright green. Unlike healthy ones, burnt leaves are characterised by brown, ring-shaped spots. The leaves affected by aphids display black dots as their defining feature. The leaves affected by caterpillars can be distinguished by characteristic leaf deficiencies and deep perforations, serving as their most prominent characteristic. Mite-affected peppers exhibit wrinkled leaves with dark green layers. In the worm-infected group, the formation of white lines on the leaf surface is identified as a key distinguishing detail. Finally, leaves infected with mildew are primarily characterised by yellow decay and spots. Comprehensive pre-processing based on that guidance, combined with high-quality photo lighting, builds up distinguishing charac  
Figure 8: Confusion matrix of the classifier.  
teristics of these classes, which, in turn, have positively impacted the learning process's performance.  
To the best of our knowledge, this is the first time that aphids and caterpillar classes have been investigated. Although the private dataset specially collected for this study is slightly imbalanced, it contains more than 220 images per class, which is a reasonable average compared to the other datasets in this field and enough to make meaningful deductions.  
The proposed model is compared with recent counterparts to provide a more comprehensive judgment about accuracy. Studies are examined according to several criteria in Table 4. It is clearly shown that higher accuracies are provided for a smaller number of tasks. In addition, in these studies, the number of patterns that models need to learn is significantly smaller, resulting in higher accuracy scores. We obtained accuracy for both modified and original VGG16 models. At first glance, the approximate results should also be viewed from the aspect of processing speeds under the same conditions. It should be highlighted that the processing time is shortened from 7618 s, which is originally required, to 6750 s.  
Despite the strong performance of the proposed model, several limitations must be acknowledged. The dataset used in this study is private and relatively limited in size, particularly for certain disease classes such as aphids and caterpillars, which may affect the robustness and generalizability of the model. Predictions for the aphid class are frequently confused with images from the burnt and mildew classes. A deeper analysis of the results indicates that variations in the conditions under which the photographs were taken contributed to classification errors during the training process. That is, the performance could be sensitive to background noise and cool and warm colour tones due to variations in lighting conditions, which are unavoidable in real-world environments. Finally, it should also be pointed out that although colour enhancement techniques improved class separability, they may introduce artefacts or distortions if not properly calibrated across diverse image sources.