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@article{207825,
        author = {Nikhil D. Ghorpade and Aadi Sathyan and Diksha C. Mhatre and Ashutosh P. Kamble and Suchita S. Shete and Rashmi Sharma},
        title = {"ARTIFICIAL INTELLIGENCE IN PHARMACOGNOSY: REVOLUTIONIZING MEDICINAL PLANT IDENTIFICATION, PHYTOCHEMICAL DISCOVERY AND HERBAL DRUG STANDARDIZATION"},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {2775-2797},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207825},
        abstract = {Pharmacognosy is undergoing a major technological transition as artificial intelligence (AI), machine learning (ML), deep learning (DL), computer vision, chemoinformatics and multi-omics become increasingly integrated into the study of medicinal plants and natural products. Conventional pharmacognostic workflows remain indispensable, but they are often dependent on expert taxonomic expertise, labour-intensive microscopic and macroscopic evaluation, extensive phytochemical screening, and analytical procedures that may require substantial time and resources. AI provides an opportunity to transform these workflows by converting complex visual, chemical, genomic and analytical datasets into predictive and decision-support systems. In medicinal plant identification, computer-vision algorithms can recognize species from leaf, flower, fruit, bark and microscopic characteristics, potentially reducing taxonomic errors and improving authentication. Recent deep-learning studies have demonstrated highly accurate classification of medicinal plants from leaf images, although performance can decline when models encounter field conditions, variable illumination, developmental stages or closely related species. AI is also reshaping phytochemical discovery through prediction of bioactivity, molecular target interactions, metabolite annotation, dereplication, virtual screening, compound optimization and integration of metabolomics with genomics and other omics datasets. Recent reviews emphasize that AI-assisted natural-product research can accelerate the prioritization of chemically and biologically promising compounds while reducing dependence on exhaustive experimental screening. In herbal drug standardization, ML coupled with chromatographic, spectroscopic and metabolomic fingerprints can support authentication, geographical-origin determination, detection of adulteration and prediction of quality attributes. A recent metabolomics–ML study involving turmeric and ashwagandha reported 98% specificity and accuracy for authentication-related classifications, illustrating the practical potential of this approach. This review critically examines the emerging role of AI across the pharmacognosy research continuum, from medicinal plant identification and phytochemical discovery to herbal drug quality assessment and standardization. Particular emphasis is placed on computational methodologies, data requirements, explainability, validation, regulatory considerations and future integration with multi-omics and foundation-model technologies.},
        keywords = {Artificial intelligence; Pharmacognosy; Machine learning; Deep learning; Medicinal plant identification; Phytochemical discovery; Herbal drug standardization; Metabolomics},
        month = {August},
        }