Abstract :

This project presents a machine learning–based system for the analysis and identification of health conditions and diseases in medicinal and aromatic plants. The system uses image processing and supervised learning techniques to detect plant diseases from leaf images at an early stage. High-quality plant leaf images are collected and preprocessed to remove noise, normalize size, and enhance features. Machine learning models such as Convolutional Neural Networks (CNN) are trained to classify healthy and diseased leaves accurately. The system identifies common plant diseases by analyzing visual symptoms like spots, discoloration, texture variation, and shape deformation. This automated approach helps farmers, herbal cultivators, and agricultural researchers reduce crop loss, improve plant health monitoring, and promote sustainable cultivation of medicinal and aromatic plants. The proposed system is cost-effective, scalable, and capable of providing real-time disease detection with high