V3I11P11

A Review on Artificial Intelligence in Pharmacognosy for Medicinal Plant Identification and Phytochemical Analysis

Namburi Deepa Ramani^1*, Shaik Feroz²

Abstract

Pharmacognosy deals with the identification, evaluation, authentication, and quality control of medicinal substances obtained from natural sources. Medicinal plants present substantial biological and chemical variation, and reliable identification is therefore a prerequisite for reproducible herbal-drug research and quality assurance. Classical pharmacognosy combines organoleptic, macroscopic, microscopic, physicochemical, and chemical approaches; however, these approaches can become laborious when large numbers of samples and complex analytical datasets must be examined. Artificial intelligence (AI), including machine learning, and computer-vision methods, offers an additional analytical layer for recognizing patterns in plant images, microscopic preparations, and chromatographic or spectroscopic data. This review synthesizes the supplied thesis with concepts from standard pharmacognosy texts, emphasizing how AI can complement—not replace—botanical authentication and pharmacopoeial testing. Particular attention is given to image preprocessing, machine-learning classifiers, convolutional neural networks, phytochemical fingerprint interpretation, adulteration screening, quality control, mobile identification, and multimodal workflows. The review also discusses dataset quality, validation, interpretability, domain shift, regulatory considerations, and the need for human oversight. AI is most appropriately viewed as a decision-support technology within a validated pharmacognostic workflow. Its practical value depends on authenticated reference material, representative datasets, transparent performance, evaluation, and confirmation by established pharmacognostic and analytical methods. [1][8]

Keywords:

Pharmacognosy; medicinal plants; artificial intelligence; machine learning; deep learning; convolutional neural networks; plant identification; phytochemical analysis; herbal drug authentication; quality control.