AI Based Plant Identification and Medical Property Analysis

Medicinal plants play a crucial role in traditional and modern healthcare systems. Accurate identification of these plants is essential to ensure safe usage and effective treatment. Conventional plant identification methods rely heavily on expert botanical knowledge and manual observation, making them time-consuming, subjective, and unsuitable for large-scale applications. This paper proposes an Artificial Intelligence (AI)-based system for automatic medicinal plant identification and medicinal property analysis using deep learning techniques. A Convolutional Neural Network (CNN) with transfer learning is employed to classify plant species from leaf images. Once identified, the system retrieves verified medicinal information such as health benefits, traditional uses, and precautions from a structured knowledge database. The system is implemented as a web-based application, enabling real-time plant identification through image uploads. Experimental results demonstrate that the proposed system achieves high classification accuracy, fast response time, and reliable performance under varying image conditions. The proposed approach bridges the gap between visual plant recognition and practical medicinal knowledge, making it useful for students, farmers, researchers, and healthcare practitioners.