Abstract : This project introduces an automated solution for detecting plant diseases using a deep learning approach based on Convolutional Neural Networks (CNNs). The system classifies images of plant leaves to accurately identify various diseases, helping farmers and agronomists diagnose crop health issues at an early stage. By automating this process, the solution offers significant improvements over traditional manual methods, which can be time-consuming and prone to human error. The model is trained on a large dataset of labeled leaf images, representing different plant species and diseases. To enhance the model's performance and generalization, image augmentation techniques such as rotation, zooming, and flipping are applied. This approach helps the model handle variations in the dataset and improves its accuracy. The ultimate goal of the project is to develop a reliable, scalable, and user- friendly tool for plant disease detection, enabling proactive crop management and reducing losses in agriculture. The system can be integrated into real-time applications for use in farming and agricultural decision-making. Keyword: Deep Learning, Convolutional Neural Networks, Image Processing, Plant Disease Detection, Image Classification, Data Augmentation. --------