Automated Medicinal Plant Identification Using Deep Learning for Improved Healthcare

The identification of medicinal plants is crucial for healthcare, drug manufacturing, and environmental management. Traditional manual identification methods are often timeconsuming and prone to errors, potentially leading to adverse health effects. To address this challenge, this project proposes an automated system for medicinal plant classification using deep learning. Leveraging convolutional neural networks (CNNs) with Xception-based feature extraction, the system ensures high accuracy and classification of medicinal plants. Additionally, it incorporates a symptom-based recommendation module, enabling users to input symptoms and receive suggestions for the appropriate medicinal plant. By automating plant identification and providing targeted recommendations, this system enhances efficiency, reduces errors, and facilitates safer and more effective use of medicinal plants in healthcare.