The bell pepper is a fruit that is often misunderstood as a vegetable due to its usage. The plant which grows bell pepper grows for about three to six feet. These plants do not require intensive care but some necessary steps have to be taken to increase the productivity of the plant. Plant diseases like bacterial spots can heavily affect growth. The consequences of such diseases can be reduced if they’re detected in their early stages. This research aims the comparison of two Deep Learning (DL) models which were developed using VGG-16 architecture and the AlexNet. For training and testing the efficiency of the models, images of the bell pepper plant’s leaf are obtained from Kaggle. This data acquired further undergoes some pre-processing techniques like resizing and rescaling to reach the desired format. The cleaned data is then split into three divisions. One of them is used to train both DL models and the second one is used to validate the models. The last part is used to test the accuracy and loss of the two models. In the end, the accuracy and loss of both the models are compared and the results are tabulated.