The majority of conventional procedures for diagnosing plant diseases rely on human visual observation and inspection. This method, however, is time-consuming and involves considerable human work and specialist knowledge. Recent advancements in computer vision and deep learning offer a potential avenue for the development of a plant disease diagnosis system that enables quick disease identification across broad spatial areas with minimal human participation. In this paper, we developed a deep learning strategy for plant leaf disease classification problems and performed a variety of experiments to evaluate the performance of ResNet50, CNN, AlexNet, and DesNet169 state-of-the-art neural network architectures. The proposed models were trained using the colab platform with the PlantVillage dataset consisting of 54,305 photos across 38 plant disease classes. We assessed each architecture using four distinct performance metrics: accuracy, precision, recall, and F1-score. The DesNet169 neural network architecture surpassed all other Convolutional Neural Network architectures, producing an accuracy of 99.10% after 70 epochs of training.