## 2 Literature Review  
The development of technology and the introduction of artificial intelligence (AI) has empowered scientists and researchers to perform plant disease detection utilizing a deep learning convolutional neural network. Deep learning is a machine subfield that has been used by researchers for many artificial intelligence applications. For instance, [PERSON] et al. developed an abnormal breast identification model by using a nine-layer convolutional neural network [2]. However, in the context of this study, works on deep learning for the detection of plant diseases after images have been reviewed. An Updated Faster R-CNN architecture developed by changing the parameters of a CNN model and a Faster R-CNN architecture for automatic detection of leaf spot disease (_Cercospora beticola Sacc_) in sugar beet were proposed by [PERSON] and [PERSON] [3]. Their proposed method for the detection of disease severities by the imaging-based expert systems was trained and tested with 155 images, and according to the test results, the overall correct classification rate was found to be 95.48%. A vision-based system to detect symptoms of leaf scorch on leaves of Olea europaea infected by _Xylella fastidiosa_ was developed [4]. In this work, the algorithm discovered low-level features from raw data to automatically detect veins and colours that lead to symptomatic leaves. Leaf scorch was detected with a true positive rate of 98.60 \(\pm\) 1.47%. The model was developed with a convolutional neural network that was trained in the stochastic gradient descent method [4]. The literature shows that the deep learning models which were used for plant disease detection had different performance accuracies that were determined by the model parameter tuning and regularization methods. [PERSON] et al. used the pre-trained networks with Image Net, a famous dataset that has labelled images ready to be used as training data [5]. Their approach improved the performance when compared with other state-of-the-art techniques; it achieved a validation accuracy of at least 91.83% when trained on the public data set. Regardless of a complex background in the images, their proposed approach achieved a normal precision of 92.00% for the rice plant class of images [5]. [PERSON] et al. [6] used deep learning for the detection of potato tuber disease. The basic architecture chosen for this problem was a CNN developed by the Visual Geometry Group (VGG) from the University of Oxford, U.K., named CNN-F due to its faster training time [3]. In their model, several new dropout layers were added to the VGG architecture to deal with problems of over fitting, especially due to the relatively small dataset. The required input image size of their fine-tuned VGG network was a 224 \(\times\) 224 matrix. The CNN comprised eight learnable layers, the first five of which were convolutional, followed by three fully connected layers and ending with a soft max layer [6]. Training CNNs usually requires a large amount of labelled data in order to perform a good classification [6]. Therefore, two methods were used for data augmentation: mirroring creates additional examples by randomly flipping the imagesused in training; cropping was also used, cropping the image randomly to different sizes, while keeping the cropped image's minimum size to 190 \(\times\) 190, helped to achieve data diversity [6]. [PERSON] et al. conducted a study around solving the current limitations on the use of deep learning for the detection and classification of plant diseases [7]. In their work, they used two approaches to deal with the issue of data augmentation. The first one was the augmentation by means of traditional methods, and the second one, the art style Generative Adversarial Networks (GANs) [7]. A summarized review of deep learning in plant disease classification without considering their severities is shown in Table 1.  
\begin{table}