This research report presents the development and evaluation of a deep learning-based system for the automated detection of plant diseases through leaf image analysis. Aimed at addressing the significant economic impact of crop diseases in agriculture, the project leverages a Convolutional Neural Network (CNN) model trained on the publicly available Plant Village dataset. The implemented system processes leaf images through stages of pre-processing, augmentation, and feature extraction to classify diseases in crops such as tomato, potato, and maize. The model achieved a high classification accuracy of 96.5%, with supporting precision, recall, and F1-scores all above 95%. The study successfully demonstrates the technical feasibility of using image processing and deep learning for accurate, rapid disease identification. A key innovation proposed is the deployment of this model on a mobile application, which would provide farmers with an accessible tool for early disease detection and improved crop management, thereby enhancing agricultural productivity. The report also discusses the current limitations and potential future integrations with IoT and advanced imaging technologies for broader field application.