# Pep-VGGNet: A Novel Transfer Learning Method for Pepper Leaf Disease Diagnosis
## 1 Introduction  
Peppers are fruits that belong to the nightshade family. They are excellent low-calorie sources of vitamins A and C, antioxidants, potassium, folic acid, and fibre, and their consumption is associated with a reduced risk of certain chronic illnesses, such as cancer and heart disease. In addition to being a popular ingredient in many dishes, peppers can be enjoyed raw, making them a highly versatile component of a well-balanced diet. In addition, peppers contain various bioactive compounds that are of interest for pharmaceutical and cosmetic applications [1, 2]. Peppers are warm-season crops harvested under glass for an average of two months but require delicately balanced growing conditions. Hence, their cultivation can be viewed from two interrelated perspectives: maintaining high productivity and nutritional value while minimising pesticide use. Although dangerous to human health, pesticides are still indispensable in the fight against viruses, bacteria, fungi, and other harmful organismsknown to cause various diseases in pepper plants, affecting multiple parts of the plant [3]. Some of those diseases particularly target the leaves, leading to distinct and noticeable visual signs and symptoms [4]. Common diseases affecting the leaves of greenhouse peppers include mildew, mites, worms, aphids, and blight. Among these, aphids and worms are particularly detrimental. A timely diagnosis is essential for timely treatment to prevent the spread of the disease. Yet, another vital advantage of the timely diagnosis is preventing unnecessary and over-spraying. Conventional methods such as observations, laboratory kits, and biochemical tests have traditionally been used in agriculture to detect greenhouse pepper diseases. However, diagnosis using these methods is, for various reasons, time-consuming and costly. At this stage, AI, which enables machines to mimic human-like behavior, seems to be a potential problem-solver for this and similar multi-parametric tasks [5]. In recent years, various DL models have become very popular due to their high effectiveness and practicality. With the development of science and technology, they have outperformed many traditional models in various fields of industry and medicine [6; 7; 8; 9]. In addition, DL algorithms, known for their high performance in object detection and classification, have been widely tested in agricultural diagnostics [10; 11; 12], and some of those are focused specifically on healthy leaves and leaves with bacterial spots of pepper with accuracy up to 83.89% [12; 13]. Referring to previous studies, DL algorithms, such as AlexNet, SqueezeNet, and Modified ResNet50, have been tested in disease diagnosis of various plants with good results. Still, few of them are related to pepper diseases [14]. Both the limited number of datasets in this issue and the limited number of images in existing sets are important obstacles. Refers to dataset classification using the VGGNet architecture, which has also been investigated in several studies, with conspicuous accuracy results achieved [15]. One of those has been obtained with an extensive open dataset comprising approximately 48,331 images of healthy and diseased plant leaves [16]. Notable results have been reported using pre-trained AlexNet and VGGNet models. The most widely tested DL algorithms typically consist of the input, hidden, and output layers. These algorithms are primarily applied to images from the PlantVillage public dataset [17]. It is notable that, differently from the studies mentioned above, this study focuses on diagnosing pepper leaf diseases using the modified VGG16 Net algorithm, trained on images from a private dataset. VGG16 Net is chosen due to its high accuracy in handling complex datasets. To optimise the training process, the hidden layers of VGG16 Net were frozen, and three additional layers were introduced to fine-tune, effectively reducing the duration of the training. Unlike previous studies, this study incorporated user-controlled adjustments to evaluate the algorithm's performance.