Identifying and diagnosing plant diseases presents a considerable challenge. Recognition and prevention of crop diseases are important for maintaining healthy plant growth to ensure sustainable supply and food security for the world’s fast-increasing population. Manual examination of plant diseases is costly, slow, non-scalable, labor-intensive, and error-prone. Farmers have traditionally used manual methods to diagnose and classify plant leaf diseases, which can be imprecise and impracticable for large-scale applications. Farmers have the potential to minimize losses and enhance crop productivity through the application of automated image processing techniques. Researchers have established a variety of techniques to identify and classify plant leaf diseases by analyzing images of affected leaves. In this work, we analyze two attention mechanism-based models, SE_SPnet and Res4net-CBAM, and compare these two models with four standard CNN models, Resnet50, VGG16, DenseNet121, and InceptionV3. We evaluate these models using datasets of leaf diseases from three distinct plants: rice, tea, and maize. The results of the experiments show that Res4net-CBAM is the best model. It had an average accuracy of 99.78% on the rice leaf disease dataset, 98.27% on the tea leaf disease dataset, and 97.97% on the maize leaf disease dataset, which was better than the other models.