# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 3 Methodology
### Feature Extraction and Classification  
The feature extraction process in our study extracts meaningful information from the input sesame leaf images. The proposed models perform feature extraction and classification using a convolutional neural network. In recent times, deep convolution neural network techniques have exhibited very high performance in different areas, such as image classification and other fields [23]. The convolution layer has the purpose of extracting features from the inputs, i.e., horizontal edges, vertical edges, pixel values, and so on. It learns features at different levels of abstraction as the layer increases. Based on the extracted features, the fully connected layer performs classification into different classes, and the results are transferred through the output layer (Softmax layer) [24]. The structure of the proposed model training, validation, and testing phases are presented below. Since the proposed model was developed based on a deep convolutional neural network, it consisted of several layers that represented different building blocks of the convolutional neural network. The layers and parameters that we applied in the proposed model to learn the features predict the classes based on the extracted features, and produce a classification result as the output are discussed below. Input Layer: We used an input size of 299 \(\times\) 299 \(\times\) 3. We selected the input size by referring to the performance of the proposed models using different image sizes. This input size allowed our model to perform well since large patterns in the image are more visual. In addition, we used this size to compare model performance with that of recent models that have similar input sizes. Convolution Layer: The proposed model consisted of 10 convolution layers with different characteristics and parameters. In the convolution layer, there are five parameters. The first parameter, which is filter size, is one of the main parameters that allows the model to detect the features. As the filter size increases, the detection rate also rises since the filters can cover larger pixels and learn features from those pixels simultaneously. The layer learns the features by localizing the regions and scanning through an image based on the filter size. Therefore, we specified the proper region to be localized at a given time using this parameter. In the proposed model, we used one by 1, 3 by 3, 5 by 5, and 7 by seven as filter sizes. The number of filters, which is the second parameter, refers to the neurons in the output of the convolutional layer. In the proposed model, we used 8, 16, 32, 64, 96, 128, 160, and 192 filters. As we moved down the network, we increased the number of filters since an increased number of neurons provides more strength and capability to the proposed network. The third parameter is stride. We used one stride value that shifted the pixels throughout the network. Since we did not want to reduce the size of the output in the convolution layer, we used a padding value of \"same\", which implies that the size of the output is equal to the input size with a stride value of \(1\). This was used to control the output size of the layer. The parameters are selected based on the cases that we classified images into. In bacterial blight-infected leaves, the symptoms are small spots that are distributed over the leaf, and each spot can cover around 7 to 9 pixels in the image. In contrast, the phyllod-infected leaves seem healthy when we look closer because the symptoms are different, changing the structure or size of the leaf and reducing internode distance; the symptoms may cover 14 to 25 pixels. Therefore, for such cases, we selected \(1\times 1\) to \(7\times 7\) filter sizes since we agreed that such sizes could cover the pixels of interest and learn features from such pixels at the same time. The other parameters were also selected based on our dataset.