Recognition and quality mapping of traditional herbal drugs: way forward towards artificial intelligence.

The use of traditional herbal drugs derived from natural sources is on the rise due totheirminimal side effects and numerous health benefits. However, a major limitation is thelackof standardized knowledge for identifying and mapping the quality of these herbal medicines. This article aims to provide practical insights into the application of artificial intelligence for quality-based commercialization of raw herbal drugs. It focuses onfeatureextraction methods, image processing techniques, and the preparation of herbal images forcompatibility with machine learning models. The article discusses commonly usedimageprocessing tools such as normalization, slicing, cropping, and augmentation to prepareimages for artificial intelligence-based models. It also provides an overviewof global herbal image databases and the models employed for herbal plant/drug identification. Readers will gain a comprehensive understanding of the potential application of various machinelearning models, including artificial neural networks and convolutional neural networks. The article delves into suitable validation parameters like true positive rates, accuracy, precision, and more for the development of artificial intelligence-based identificationandauthentication techniques for herbal drugs. This article offers valuable insights andaconclusive platform for the further exploration of artificial intelligence in the field of herbal drugs, paving the way for smarter identification and authentication methods. [ABSTRACT FROM AUTHOR]