Automated Medicinal Plant Identification through Image Processing and Machine Learning

This research addresses the pressing challenge of accurately identifying medicinal plants in India's Ayurvedic Pharmaceutics, aiming to mitigate issues stemming from morphological variability and market adulteration. Traditional methods of plant identification are hindered by laborious processes and the potential for error, underscoring the need for an automated solution. Leveraging the power of advanced image processing and machine learning, our proposed software framework integrates the Xception model and early stopping fine-tuning to automate plant identification based on plant images. The methodology involves curating a comprehensive dataset of labelled plant images representing diverse medicinal plants, subsequently employing image preprocessing techniques to extract salient features like color, texture, and shape. The Xception model, renowned for its prowess in image recognition tasks, serves as the backbone architecture for both feature extraction and classification. To further optimize model performance and mitigate overfitting, early stopping fine-tuning techniques are applied. Through rigorous experimentation and validation, our research demonstrates the efficacy of this approach in accurately identifying medicinal plants, thereby fostering confidence and trust within the herbal medicine supply chain. By seamlessly integrating modern technological advancements with traditional botanical knowledge, our framework not only streamlines the identification process but also contributes to the sustainable utilization of herbal medicine resources. Ultimately, this research represents a pivotal step towards establishing a robust and reliable system for identifying medicinal plants, facilitating informed decision-making, and promoting the long-term viability of herbal medicine practices in India and beyond.