Intelligent System for Medicinal Plant Identification and Personalized Health Recommendations

In the present sectors of healthcare and herbal medicine, the integration of artificial intelligence with traditional medicinal knowledge offers a new approach to automated diagnosis and disease treatment recommendation. This research proposes a web application system for medicinal plant identification and disease treatment recommendation by using the deep learning techniques. The system consists of a MobileNetV2-based Convolutional Neural Network (CNN) for image-based medicinal leaf identification and a text-based query analysis module for disease treatment according to the symptoms. The input module consists of dual search function which allows plant identification through leaf image analysis and text-based query input for leaf name to image identification, ensuring that it has user-friendly interaction. Additionally, a prescription generation module automatically produces disease specific treatment recommendations based on patient details such as age, gender, and seasonal conditions. The system architecture is designed to use FastAPI for the backend, React for the frontend and is deployed on AWS EC2 for scaling and performance consistency. Through this experimental evaluation on various plant datasets combined with curated medical text data, this system achieves remarkable classification accuracy along with effective treatment mapping which ensures its practical usage and scalability in real-world scenarios.