Plant disease detection is a critical task in agriculture to prevent significant losses due to disease spread. The manual examination process used in traditional disease detection methods takes a lot of time and labor. This research presents a plant disease detection system using deep learning, specifically leveraging the InceptionV3 architecture, a type of Convolutional Neural Network (CNN). Our approach demonstrates improved accuracy and speed in identifying plant diseases, contributing to more efficient agricultural practices. The model achieved a validation accuracy of 96%.