Agricultural productivity faces a substantial risk because crop diseases lead to major losses in yield quantity and quality, despite agriculture being essential for food security. Diseases affecting crops pose a significant threat to agricultural production by causing severe reductions in both yield quantity and crop quality, even though agriculture serves as a cornerstone of global food stability. Traditional plant disease diagnostic approaches are resource-intensive and slow, often requiring expert skills, making them inaccessible to many farmers. This paper presents a system that demonstrates automated plant disease identification using Convolutional Neural Networks (CNNs), which excel at image classification. The CNN learns to categorize several diseases through training on labeled leaf images from both healthy and diseased plants. The model’s user-friendly gradio interface allows users to upload images and receive quick diagnoses. It enables early disease detection, providing farmers with fast and reliable results to support timely treatment decisions. The experimental findings demonstrate the system’s robustness and its ability to operate effectively in agricultural environments lacking sufficient resources.