This project focuses on developing a machine learning-based system for detecting plant diseases, providing valuable support to farmers, botanists, and researchers. The aim is to improve agricultural productivity and research efforts through automated plant health monitoring. The solution includes a user-friendly and responsive interface built with Streamlit, a Python framework that facilitates the creation of web applications, enabling efficient interaction for users, whether they are in farming or research. The system leverages Convolutional Neural Networks (CNN) with a fine-tuned VGG16 pre-trained model, utilizing transfer learning to accurately classify plant diseases based on leaf imagery. A diverse dataset of plant diseases is used for training, with advanced image preprocessing techniques applied to improve classification accuracy. This solution ensures a scalable, precise, and user-friendly method for disease detection, facilitating seamless adoption into modern agricultural workflows.