A Comparative Analysis of the Adam and RMSprop Optimizers on a Convolutional Neural Network Model for Predicting Common Diseases in Strawberries

A simplified Convolutional Neural Network (CNN) model for strawberry disease classification is proposed in this paper. The model is trained and evaluated on a dataset of 2500 strawberry images with seven different types of diseases. The results show that the model can achieve an accuracy of 0.98 on the training and 0.97 on the validation sets. The model can also identify the most common strawberry diseases, such as angular leaf spot, blossom blight, gray mold, leaf spot, and powdery mildew leaf. However, the model does make some errors, such as misidentifying angular leaf spot as leaf spot and gray mold as leaf spot. These errors are likely due to the similarities between lesions of different diseases at various times and angles. Additionally, it is possible that the model could be improved by increasing the dataset size or the number of epochs. We also compare the performance of two optimizers, Adam and RMSprop, for training the CNN model. The results show that the RMSprop optimizer achieves a higher accuracy than the Adam optimizer. This is likely due to the fact that the RMSprop optimizer is more robust to noisy gradients.

A Comparative Analysis of the Adam and RMSprop Optimizers on a Convolutional Neural Network Model for Predicting Common Diseases in Strawberries Keywords:

Adam , Convolutional Neural Network , Rmprop