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

## TL;DR

This paper introduces a new dataset of South Indian medicinal plants to improve AI-based species identification and conservation efforts.

## Contribution

The novel contribution is the creation of SIMPD, a high-quality dataset for South Indian medicinal plants with detailed metadata for AI applications.

## Key findings

- SIMPD includes high-resolution images of diverse medicinal plant species from South India.
- The dataset supports machine learning tasks like classification and segmentation under real-world conditions.
- It aims to bridge traditional ethnobotanical knowledge with modern computational methods.

## 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