Automated Identification of Indian Medicinal Plants using Deep Learning

Medicinal plant traditional knowledge is intricately embedded in the health care practices and culture of India’s rural indigenous communities. These communities have used the identification, collection, and use of herbs to treat sickness for many generations. India possesses almost 10,000 plant species with known therapeutic value, many of which are serve as the foundation for Ayurveda, one of the most traditional and holistic systems of medicine in the world. Even with this rich biodiversity, most of these plants have not been incorporated into the official Ayurvedic Pharmacopoeia due to time-consuming and labor-intensive procedures required for standardization and scientific authentication. Conventional identification methods that are based primarily on visual examination can be serious health hazards and are susceptible to misclassification. To overcome these shortcomings, the current study suggests an automated, deep learning based image classification model for precise identification of medicinal herbs. Through model training using high quality images of leaves and plants, the strategy focuses on improving classification accuracy, reducing human error, and offering an efficient, intelligent solution to aid ethnobotanists, Ayurvedic physicians, and teachers working in the fields of traditional medicine and plant science.