This research presents an automated approach for plant disease detection using deep learning, specifically leveraging Convolutional Neural Networks (CNNs). The system analyzes images of plant leaves to accurately classify diseases, assisting farmers and agronomists in early diagnosis of crop health issues. By eliminating reliance on traditional manual inspection, which is often timeconsuming and error-prone, the proposed solution enhances efficiency and precision. The model is trained on an extensive dataset of labeled leaf images, covering various plant species and diseases. To improve performance and generalization, image augmentation techniques such as rotation, zooming, and flipping are employed, allowing the model to handle diverse variations in the dataset. The objective of this study is to develop a scalable, reliable, and user-friendly system for plant disease detection, facilitating proactive crop management and minimizing agricultural losses. Furthermore, the system has the potential to be integrated into real-time applications for enhanced decision-making in farming and agriculture. Keywords: Deep Learning, Convolutional Neural Networks, Image Processing, Plant Disease Detection, Image Classification, Data Augmentation.