Abstract :

Medicinal and Aromatic Plants (MAPs) are widely used in pharmaceutical and healthcare industries, but their quality is often affected by plant diseases. Traditional disease detection methods rely on manual inspection, which is time-consuming, costly, and prone to human error. This project aims to develop an automated system for analysing and identifying health conditions and diseases in medicinal and aromatic plants using Machine Learning. The proposed system uses image processing techniques to analyse leaf images and detect disease symptoms at an early stage. Convolutional Neural Networks (CNNs) and supervised learning algorithms are employed for accurate disease classification. The system is implemented using Python with libraries such as TensorFlow/Keras, OpenCV, and NumPy. A web-based interface developed using Flask allows users to upload images and view results in real time. This approach improves accuracy, reduces diagnosis time, and supports sustainable cultivation of medicinal