Medicinal Plant Identification Using CNN and Pytorch Lightning

Medicinal plants have long been integral to healthcare, providing natural therapeutic compounds. With the growing interest in alternative medicine, accurate identification of these plants is crucial. Traditional methods of plant identification rely on expert botanists and manual examination, which are both time-consuming and susceptible to errors. This paper seeks to develop a reliable system for identifying medicinal plants through the use of Convolutional Neural Networks (CNNs) and the PyTorch Lightning framework. Our approach leverages deep learning to automate plant identification, addressing the increasing demand for herbal medicine authentication. Using a dataset of annotated medicinal plant images, we design a CNN architecture to extract features and accurately classify species. Transfer learning fine-tunes pre-trained models to improve classification performance, even when training data is limited. Performance indicators including precision, recall, and F1-score are used to assess the system. The goal is to create a reliable tool for botanists, pharmacists, and herbal medicine practitioners, aiding in the authentication and preservation of traditional medicinal knowledge. This automated identification system promises to enhance the precision and effectiveness of the classification of medicinal plants, supporting sustainable use and conservation efforts.