Medicinal plants of South India: A comprehensive dataset for species identification

The identification and classification of medicinal plants are crucial for botanical research, traditional medicine, and AI-driven applications. However, the absence of a standardized, high-quality dataset limits advancements in automated species recognition. This study introduces SIMPD Version 1 (South Indian Medicinal Plants Dataset), a curated dataset comprising high-resolution images of diverse medicinal plant species native to South India. The dataset integrates detailed taxonomic classifications and metadata to facilitate precise species identification and biodiversity analysis. Images were acquired under real-world conditions, considering variations in illumination, pose, and environmental factors to enhance dataset robustness. SIMPD is designed to support machine learning applications, particularly in image-based plant classification, object detection, and segmentation tasks. By providing an extensive dataset for AI-driven research, this work aims to bridge the gap between traditional ethnobotanical knowledge and modern computational methodologies, fostering advancements in medicinal plant classification, conservation, and ecological research.

Authors

Institutions

Publication Details

Journal
Data in Brief
Published
2025-05-20
DOI
https://doi.org/10.1016/j.dib.2025.111660
Primary Topic
Ethnobotanical and Medicinal Plants Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Medicinal plants of South India: A comprehensive dataset for species identification

Sabari Nathan, Muthukumar Arunachalam, D. Elumalai, T. Gopu
Data in Brief
Ethnobotanical and Medicinal Plants Studies
article

Medicinal plants of South India: A comprehensive dataset for species identification

Sabari Nathan, Muthukumar Arunachalam, D. Elumalai, T. Gopu
article en

Abstract

The identification and classification of medicinal plants are crucial for botanical research, traditional medicine, and AI-driven applications. However, the absence of a standardized, high-quality dataset limits advancements in automated species recognition. This study introduces SIMPD Version 1 (South Indian Medicinal Plants Dataset), a curated dataset comprising high-resolution images of diverse medicinal plant species native to South India. The dataset integrates detailed taxonomic classifications and metadata to facilitate precise species identification and biodiversity analysis. Images were acquired under real-world conditions, considering variations in illumination, pose, and environmental factors to enhance dataset robustness. SIMPD is designed to support machine learning applications, particularly in image-based plant classification, object detection, and segmentation tasks. By providing an extensive dataset for AI-driven research, this work aims to bridge the gap between traditional ethnobotanical knowledge and modern computational methodologies, fostering advancements in medicinal plant classification, conservation, and ecological research.

Data in BriefVol. 61
National Society of Professional Engineers (US), Institute of Engineering (NP), Kalasalingam Academy of Research and Education (IN)
Zero hunger
Openalex Percentile: Top 92%
Ethnobotanical and Medicinal Plants Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Medicinal plants of South India: A comprehensive dataset for species identification — Sabari Nathan, Muthukumar Arunachalam, et al. · Data in Brief (2025) | TGRS Research Map | TGRS