The performance of many deep learning models for the classification of multi-class plant diseases is examined in this study.  Accurate and effective solutions are necessary because plant disease identification is crucial to agricultural productivity.  Publicly accessible datasets of plant disease images are used to assess deep learning models, especially convolutional neural networks (CNNs), and transfer learning architectures such as ResNet and VGGNet.  These models are compared in the study according to their generalizability, accuracy, and computing efficiency.  The results are intended to shed light on the best deep learning methods for managing and detecting plant diseases in the real world.