# A Mobile-Based System for Detecting Ginger Leaf Disorders Using Deep Learning
## 2 Related Work  
This section describes various research studies that were carried out to detect plant leaf disorders using classical and advanced deep learning methods. The spread of crop diseases might harm the economy. Traditionally, crop disease diagnosis is made manually, which is time-consuming and lacks accuracy. The authors in [20] performed image binarization, contour extraction, and fox fitting techniques using deep learning. This work utilized different deep learning models and achieved 73% accuracy. However, the adopted models showed poor performance on the other datasets. Moreover, the recommended model, however, requires a lot of computation during training to identify various diseases. Identifying citrus plant diseases was addressed. The authors in [21] employed K-means, classification, and neural network techniques. The study emphasized the traits, advantages, and disadvantages associated with citrus. The study also showed that new technologies would be needed to identify and categorize citrus plants in the future. Additionally, they mentioned that citrus plants' automatic recognition and classification are still in their early stages. In [22], the authors developed pipelines based on fuzzy, support vector machine and neural network for plant leaf disease detection. Although the study achieved significant results, the authors highlighted the pros and cons of computer vision-based methods in plant disease detection. This study also suggested exploring new tools and techniques for disease identification at different stages. For the task of detecting wheat leaf disease from finely grained image categorization in [23], the authors employed improved CNN. The suggested model was implemented with many neurons, data sources, and connecting channels. The findings showed that VGG16 with AlexNet can achieve approximately 90% accuracy. Furthermore, the study highlighted that other models, such as GANs, would produce better results using a large dataset. In [24], the authors focused on the fusarium head blight disease that affects wheat crops. Images of wheat leaves are processed to identify the damaged area using CNN and image processing techniques. The model correctly identifies the crop's sick regions in training with a mean average precision of 92%. The outcomes surpass those of k-means and Otsu's techniques. To more accurately diagnose the unhealthy components, this approach needs vast datasets. The CNN classification technique was exploited in study [25] to identify the strengths and weaknesses of works that used a CNN to detect crop diseases. The study proposed developing a more balanced and reliable agricultural tool for food production. In [26], deep learning, machine learning, transfer learning, and deep convolutional neural network were applied. The proposed model successfully classified 38 different diseased classes. The results showed 96.46% accuracy by the proposed model. This accuracy exceeds standard machine learning techniques. In [27], the diseases and pests of corn crops were captured at early stages from the field. The images were segmented by using image texture-based and iterative clustering methods. Then, the obtained features from the segmented images were used in the classification process. Moreover, the classification was performed via a multi-class support vector machine (SVM). The results showed 52% accuracy for the pest detection problem. However, the most common pest attack on the corn crop is an aphid, which is not considered in this study. In [28], authors explored the application of deep learning to identify rice plant-associated diseases. This study demonstrated that ResNet-101, VGG-16, and YoloV3 are robust to blurred and irregularly shaped images. However, when creating frames, they cannot extract the right frames because the main features of frames are fewer, while new frames are spectra that result in a waste of computation resources. In [29], authors worked on identifying the ginger plant diseases at the initial stage.