Mainly maintaining food security and increasing the production rate of crops and agricultural products greatly depends on the accurate identification and categorization of the diseases in plants. This research seeks to what extent does convolutional neural network (CNN) algorithms with ResNet approaches in plant disease identification systems. CNN and ResNet, the two modern deep learning approaches, are used for diagnosing pictures of a diseased plant leaf and classifying them according to their disease type. Our main concern regarding the images is to apply preprocessing to enhance the quality of the images and apply feature extraction that will help capture the patterns of diseases most efficiently. In our experimental analysis, both the methods are compared in terms of accuracy, precision, recall, and F1-score on benchmark datasets. According to the findings, algorithms of CNN are found to outperform ResNet modalities in most of the occasions for increasing reliability of plant disease identification jobs, such as convolutional neural networks (CNNs) could provide very good results because it is capable of learning the hierarchical features from raw photos that should lead to better classification of diseases. Finally, it is necessary to acknowledge the role of Resnet50 and CNN-based approaches in general with regard to the issues of plant diseases diagnosis and management, which may lead to the improvement of agricultural practices and food production.