# Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis
## 2 Related Works  
The literature survey focuses on prior studies on detecting plant diseases, which were published in the last decades, employing DL algorithms, such as AlexNet, DenseNet, VGGNet, MobileNet, and ResNet. Ten studies, chosen because they resemble our work, are chronologically summarized below. Since it was first proposed in 1998 by [PERSON] et al. for image recognition, DL has been significantly developed and enriched through various studies and research [18]. However, paving the way for agricultural tasks was a challenge, mainly due to the lack of an adequate database. Until recently, DL studies in agriculture, especially in plant disease diagnosis [19], were almost non-existent except for sporadic practical applications. In the last couple of years, researchers have focused on improving the accuracy of plant disease detection by employing various algorithms and applying different databases to a wide range of plant species. A crucial milestone in AI applications for agriculture tasks was the release of the PlantVillage database, which comprises 38 classes of 14 plant species and remains one of the most extensive datasets in the field. [PERSON] et al. utilised a CNN model to identify tomato leaf diseases using the PlantVillage database [20; 21; 22]. To further enhance the classification process, they implemented the learning vector quantisation (LVQ) algorithm, which achieved an accuracy rate of 88%. Demonstrating its effectiveness with such accuracy paved the way for the broadened usage of CNN-based classification techniques for detecting leaf diseases. In the study, tomato leaves are classified into five classes, including four disease classes and one healthy class. The same year, [PERSON] and his team employed pre-trained AlexNet and VGGNet models to categorise tomato leaf images from the same dataset into seven distinct classes [23]. Their experimental results demonstrated a notable accuracy rate of 97.4%, primarily attributed to the AlexNet architecture. Despite the methodological similarities between studies, their study achieved a significantly higher accuracy than other studies' findings. Another study based on VGGNet was conducted by [PERSON] and colleagues, who evaluated and compared the performance of various architectures trained on an open database comprising approximately 87,000 photographs of healthy and diseased plant leaves for 25 plant species, including peppers [24]. Among the architectures compared, VGGNet achieved an accuracy rate of 99.53%, surpassing other models in this dataset. The following year, [PERSON] et al. proposed a combined VGGNet-AlexNet methodology tested on four databases, including the PlantVillage database [25]. Different types of plants, including peppers, were classified as healthy and diseased. Instead of plant-based accuracy, the accuracy rates are given separately in a more general form for each dataset. The best score obtained for binary classification on PlantVillage is 99.80%. Pepper leaves from the same dataset were also used by [PERSON] and colleagues, who worked on detecting one of the most common bacterial diseases, Xanthomonas campestris [13]. Their study utilised a triple-classifier VGG16-based model to classify leaf images into healthy, mildly infected, and strongly infected groups. [PERSON] proposed an alternative classification method for diagnosing infected pepper leaves by detecting bacterial spot patterns [26]. Two VGGNet models, VGG16 and VGG19, were employed separately as binary classifiers of 2475 images to distinguish healthy and infected leaves with an accuracy of around 96% and 97%, respectively. In general, advances in DL in the last decade have led to tremendous progress in classification tasks, so the fertile field of recognizing pepper leaf disease is no exception. Over time, basic binary classification gave way to more complex multiclassifications. Several five-class classification models encompassing healthy and four diseased categories have recently been reported [27]. [PERSON] et al.