Tomato Leaf Disease Detection Using Hyperparameter Optimization in CNN

In this study, we trained a convolutional neural network to detect disease in tomato leaves and then examined the effect of hyperparameters and layers used when training a convolutional neural network on the trained model. In our study, it was observed that with hyperparameter tuning, it is possible to increase the validation accuracy of a CNN trained from scratch using the plantvillage dataset from 92% to 98%.