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In this study, three deep learning models (CNN, VGG16, and VGG19) were compared for the objective was to detect plant diseases, and to accomplish this, a dataset comprising 9,127 images of plants annotated with disease labels was used to train and evaluate the model’s using accuracy, F1 score, recall, and precision as performance metrics. The results show that CNN achieved the highest overall performance in the classification task, with an accuracy of 0.97 and an F1 score of 0.95. However, the VGG16 and VGG19 models also demonstrated strong performance, with accuracies of 0.96 and 0.95, respectively. The use of deep learning for plant illness interpretation has many advantages, such as automatically extracting relevant features from the input images and scalability. However, there are also limitations such as overfitting, computational resources, and the need for high-quality annotated data. Additional …