Accurate, timely detection of plant disease is critical to protect crop from being damaged and increase agricultural productivity. Many disease identification methods are labour-intensive and only practical with an expert set of trained eyes. A mobile application for real-time plant disease detection using CNNs presented in this paper allows farmers to have a simple yet powerful access to a diagnostic tool. CNN was trained on a big collection of plant leaf images to discriminate between diseases using Keras and TensorFlow. The application was built using Flutter for cross-platform mobile development, trained model deployed on mobile devices using TensorFlow Lite, which allows offline inference. Users can capture images of affected plant leaves and get immediate diagnostic feedback as to the potential disease involved. Following data preprocessing and model optimisation, the application uses a lightweight architecture that achieves high accuracy while meeting requirements for mobile deployment. This research shows integration of AI with mobile technology can provide a scalable, efficient, and accessible solution to crop disease detection. The system as proposed is capable of improving crop health management, reducing losses, and working towards global food security.