Medicinal Plant Recognition

Abstract

This paper introduces the concept of an “Medicinal Plant Recognition” has historically relied on expert knowledge and manual classification, often leading to errors and inefficiencies. This work presents an automated medicinal plant recognition system using deep learning techniques, specifically a fine-tuned ResNet50 model, coupled with a web- based user interface built with Flask. The system enhances classification accuracy by applying a series of preprocessing steps including grayscale conversion, edge detection, thresholding, and sharpening. Users can interact with the application by uploading leaf images or using real-time camera input for immediate predictions. Upon classification, the system provides the plant's name, the confidence score, and detailed medicinal information such as benefits and potential side effects. Designed for scalability, the platform bridges traditional medicinal knowledge with modern artificial intelligence approaches, offering applications in healthcare, agriculture, education, and biodiversity conservation.