ABSTRACT Agriculture plays a vital role in sustaining global food supply, and plant diseases significantly affect crop productivity, quality, and economic stability. Conventional disease diagnosis methods rely heavily on manual inspection by experts, which can be time-consuming, inconsistent, and inaccessible to rural farming communities. In recent years, advances in deep learning have enabled automated and highly accurate plant disease classification using leaf images. This research proposes a Convolutional Neural Network (CNN)-based plant disease detection system integrated into an Android mobile application. The system is designed to capture plant images through the device camera or import them from the gallery, process the data using a pre-trained CNN model optimized with TensorFlow Lite, and generate real-time disease predictions. The solution aims to assist farmers in early disease identification, improve decision-making, and reduce crop loss. Experimental evaluation demonstrates high accuracy in disease classification across multiple plant species. The mobile application interface is designed with simplicity, enabling seamless use even by non-technical users. This research contributes an implementable, accessible, and portable plant disease detection tool with potential scalability for real-world agricultural deployment. keywords: Plant disease detection, Convolutional Neural Networks, Mobile application, TensorFlow Lite, Image classification, Deep learning.