Medicinal Herb Identification Using CNN Model

Abstract: Automated herb identification plays a crucial role in various industries such as cosmetics, medicine, and food, where the need to accurately identify different plant species is essential. However, existing methods often face challenges when dealing with complex backgrounds and a wide variety of patterns, especially in wild environments. In response to these challenges, we propose an innovative convolutional neural network (CNN) model that incorporates two key components: the Part- Information Perception Module and the Species Classification Module. The Part-Information Perception Module in our model is designed to focus the model's attention on the relevant parts of the herb, effectively suppressing background noise. This mechanism allows the model to better discern the distinctive features of the herb itself, improving overall accuracy. Furthermore, we employ depthwise separable convolution and label smoothing techniques to reduce model complexity and minimize the impact of labelling inconsistencies, enhancing both efficiency and reliability. To validate the effectiveness of our model, we conducted experiments using a diverse dataset of herb images. The results demonstrate a significant improvement in accuracy and model efficiency compared to existing methods, making it a valuable tool for herb identification in challenging environments. In addition to herb identification, we also address the complex task of recognizing medicinal plant species. To facilitate this, we have created a dataset comprising image features of plant leaves, enabling the development of an automated recognition system using machine learning classifiers. Our experiments show that the classifiers achieve an impressive average accuracy rate of over 97%, highlighting the potential of our approach in accurately identifying medicinal plant species.