# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 3 Methodology
### Feature Extraction and Classification
Batch Normalization Layer: We used this layer between convolutional layers and ReLU activation layers in all convolution operations to normalize activation and speed up the training of the proposed model. While ReLU is widely used due to its simplicity and efficiency, it has a known drawback: it can lead to dead neurons when the weights are updated such that input values remain permanently negative, preventing further learning by those neurons. To address this issue, modified activation functions such as SmoothReLU can be considered. SmoothReLU offers a continuous and differentiable alternative to ReLU, mitigating the problem of dead neurons and potentially improving the learning capacity of the network [25]. Future work could explore the integration of SmoothReLU to enhance model performance and stability. Activation Layer: We used the ReLU activation function for all convolution operations. This layer enables the network to learn complex patterns from the inputs. The ReLU function replaces the 0 and negative outputs of the neurons with 0, which deactivates the neuron, and when the output is positive, it leaves the neuron as is. This implies that the function does not activate all the neurons at the same time. Due to this, it computes faster than other activation functions, and this makes a significant difference in training time. Pooling Layer: We used mixed pooling with max pooling and average pooling methods. The max pooling layer brightens pixels, while average pooling smooths out the images and the sharp features that may not be identified. Since the images we used in our experiment did not have a constant character, to maximize the benefits of the two pooling methods, we applied both of them. Since the size of the input image is large, we used a pooling size of 2 and stride of 2 at the end of every convolution operation to reduce the spatial size. A pooling size of 2 by 2 covers 4 values and converts them to a single value to down sample the size of the output and remove redundant information. Dropout Layer: We used a dropout layer at the end of the convolution layer. This prevents the proposed model from overfitting since this layer effectively changes the underlying network architecture between iterations by randomly sets input elements to zero with a given probability. A higher number results in more elements being dropped during training. Fully Connected Layer: In this layer, the features that are learned in the convolution process are combined to identify the pattern. We used a single fully connected layer with an output size of 3, which is the number of classes that we identified with the proposed model. The identified pattern is used to classify the images. This is why we used an output size parameter similar to the number of classes in our dataset. Softmax Layer: This takes numbers from all three neurons of a fully connected layer and sums them to a single one that provides the final output corresponding to the particular class. Then, this can be used to determine classification probabilities for each classification output. In the model training process, we used 40 epochs, which implies that the training process moved 40 times over the training data to extract features and train the model. Wealso adjusted other parameters concerning the number of images that we used to train and the problem domain that we solved using the model.