# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 2 Related Work  
Researchers in [10] proposed a rule-based expert system for diagnosing and treating sesame plant disease. The system aimed to help agricultural experts identify the four common sesame plant diseases, which are phyllody, dry root rot, Phytophthoralora blight, and Alternaria blight. To develop and implement the expert system, they used the C Language Integrated Production System (CLIPS) expert system and Delphi language. This system determines the diagnosis by asking the users to choose the symptoms that appear in the sesame plants. Based on the selected symptoms, the system provides a recommendation of the disease to the users. The authors obtained encouraging results. Authors in [11] presented a study to identify leaf rust disease in coffee plants, which is caused by pathogenic fungi that attacks the underside of coffee leaves. To train and evaluate their model for rust detection, they prepared a dataset that contained 159 images of coffee leaves. Their proposed architecture contains only two convolutional layers followed by a nonlinear ReLU filter and a max pooling layer. Their approach can detect the infected area and uses morphological erosion to improve the detection for high precision. Their model achieved 95% accuracy. In [13], as the researchers discussed, they developed model to detect mango plant diseases. For the identification of the diseases, they used a dataset that consisted of 8853 total mango leaf images. The images they collected consisted of four diseases such as powdery mildew, anthracose, red rust, and golmich. In their proposed model, they retrained the ResNet50 convolution neural network system. They measured the performance of the training model across the three architectures of ResNet, which are ResNet18, ResNet34, and ResNet50. ResNet50 has 50 deep layers, but its computational time cost is only 2.5 times higher than that of the other two. Based on their experiment using the mango leaf dataset, the ResNet18 CNN, ResNet34 CNN, and ResNet50 architecture achieved 91%, 90.88%, and 91.50% accuracy, respectively. The authors concluded that the deep convolution neural network is a powerful technique to detect mango leaf diseases in the field with high precision. In [14], researchers proposed a maize leaf disease identification technique using the GoogleNet and Cifar10 convolution neural network models. They collected 500 maize leaf images with similar backgrounds from different Google websites and cleaned the images using developed Python scripts. In their image processing steps to enhance the images, they performed size and format normalization using Python scripts based on the OpenCV framework automatically. Based on the experimental result, they recorded an accuracy of 98.5% and 97.1% using the original GoogleNet and Cifar10 models, respectively, and a high accuracy of 98.9% and 98.8% from the improved GoogleNet and Cifar10 models, respectively. The researchers concluded that reasonably improving the models allows for a reduction in the convergence iteration and leads to an increase in the identification accuracy. In [15], researchers proposed a model for detecting three rice plant diseases and healthy rice plants using deep convolutional neural networks. They used 619 rice plant images from the field belonging to four classes. The number of images used for each class was 88 for leaf brown spot-infected plants, 240 for bacterial leaf blight-infected plants, 100 for sheath blight-infected plants, and 191 for healthy leaves. We trained and tested the model using various partitioning strategies: 60% of the data for training, 40% for testing, 70% for training, 30% for testing, and 80% for training, and 20% for testing. They used AlexNet to extract features and a support vector machine (SVM) to classify them. Using the above training-testing partitions, they achieved 91.37% with the training-testing partition of 80-20%. In [16], researchers proposed two convolution neural network models that detect blackleg disease in potato plants.