Abstract
Centella asiatica is a significant medicinal herb extensively used in traditional oriental medicine and gaining global popularity. The primary constituents of Centella asiatica leaves are triterpenoid saponins, which are predominantly believed to be responsible for its therapeutic properties. Ensuring the use of high-quality leaves in herbal medicine preparation is crucial across all medicinal practices. To address this quality control issue using machine learning applications, we have developed an image dataset of Centella asiatica leaves. The images were captured using Samsung Galaxy M21 mobile phones and depict the leaves in "Dried," "Healthy," and "Unhealthy" states. These states are further divided into "Single" and "Multiple" leaves categories, with "Single" leaves being further classified into "Front" and "Back" views to facilitate a comprehensive study. The images were pre-processed and standardized to 1024 × 768 dimensions, resulting in a dataset comprising a total of 9094 images. This dataset is instrumental in the development and evaluation of image recognition algorithms, serving as a foundational resource for computer vision research. Moreover, it provides a valuable platform for testing and validating algorithms in areas such as image categorization and object detection. For researchers exploring the medicinal potential of Centella asiatica in traditional medicine, this dataset offers critical information on the plant's health, thereby advancing research in herbal medicine and ethnopharmacology.