A Deep Learning Framework for Plant Disease Detection

Abstract

As a major source of nutritious food, the agriculture industry supports economies and feeds people. Yet, the production of food is severely hampered by plant diseases. Major crops like wheat (21.5%), rice (30.0%), maize (22.6%), potatoes (17.2%), and soybeans (21.4%) have significant annual output declines due to numerous diseases, according to recent studies. Since deep learning technologies have been developed, image categorization accuracy has increased dramatically. Using CNN and vision transformer models, we examine the Plant Village dataset in this study, which consists of 54,305 sample images that illustrate various plant disease species in 38 classifications. Using a focus on potato leaves and a total of 2151 samples, we evaluate the model’s performance in comparison to other models in terms of training and testing accuracy, and we obtained impressive results. The models’ respective training accuracy is 97.27% for the CNN and 94.7% for the ViT model, while their validation accuracy is 100% and 94.27%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.