Identification and Classification of Medicinal Plants using Deep Learning

Abstract

The accurate identification of medicinal plants is crucial for ensuring the quality and efficacy of herbal remedies. This paper investigates the application of deep learning for automatic medicinal plant classification using leaf images. We propose a deep learning model based on the pre-trained ResNet-50 architecture to classify medicinal plants from the LeafSnap dataset. The model leverages transfer learning to exploit pre-trained features and fine-tune them for the specific task of medicinal plant identification. We evaluate the performance of the proposed model and achieve a high accuracy of approximately 99.86%. This demonstrates the effectiveness of deep learning, particularly the ResNet-50 architecture, for automated medicinal plant classification. Our findings highlight the potential of this approach for applications such as supporting field identification, streamlining herbarium workflows, and potentially aiding in the development of novel drug discovery pipelines. Keywords- Medicinal plants, Deep learning, Convolutional Neural Networks (CNNs), ResNet-50, Transfer learning, Data augmentation, LeafSnap dataset, Image classification