International Journal on Science and Technology

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AI-Based Medicinal Plant Identification: A Survey

Author(s) Ms. Vibha Venugopala, Ms. Vidyashree N, Ms. Shreya J L, Ms. Tanushree R, Mrs. Chethana H R
Country India
Abstract In order to promote pharmaceutical research, traditional medicine and biodiversity conservation, it is essential to accurately identify medicinal plants. Manual plant classification still requires a lot of work, takes a long time and is prone to errors. Automated plant identification systems, especially those that use image-based recognition based on leaf, blossom or whole-plant traits, have been made possible by recent developments in Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL).

With an emphasis on studies released in the previous five years, this comprehensive paper examines the most recent advancements in medicinal plant identification utilizing AI, ML and DL approaches. Convolutional Neural Networks (CNNs), Transfer Learning, Support Vector Machines (SVMs) and hybrid approaches are all compared in this extensive analysis of more than 25 peer-reviewed academic publications. The survey identifies the main trends, advantages and disadvantages of the studies and groups them according to architecture, datasets and performance.

It also talks about contemporary difficulties such as visual resemblance between species, the scarcity of datasets and problems with generalization in real-time settings. This study offers insights into future research routes for more reliable, scalable and accurate medicinal plant identification systems by identifying gaps and evaluating advancements. The results are intended to assist scholars and professionals in developing intelligent plant recognition frameworks for use in agriculture, healthcare and conservation.
Keywords Medicinal Plant Identification, Artificial Intelligence, Machine Learning, Deep Learning, Convolutional Neural Networks
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-04-17
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