# Central India Medicinal Plant Dataset (CIMPD)

## TL;DR

A new dataset of medicinal plant leaves from central India is introduced to support research in health and technology.

## Contribution

The creation of the Central India Medicinal Plant Dataset (CIMPD) with 9130 leaf images from 23 species for research and machine learning applications.

## Key findings

- The dataset includes both healthy and unhealthy leaf images from 23 medicinal plant species.
- It provides detailed information such as botanical names, geographical origins, and medicinal uses.
- The dataset is intended for use in machine learning and computer vision research.

## Abstract

In the present scenario, medicinal plants play a crucial role in promoting a healthy lifestyle by protecting against numerous diseases. They also hold significant potential as a source of income, particularly for rural populations across the globe. Plants used for herbal medicine are known as medicinal plants, and each part of these plants may be utilized for medicinal purposes. Further, medicinal plants are beneficial in enhancing the human immune system.

In this research, a new medicinal plant named as Central India Medicinal Plant Dataset (CIMPD) has been developed to support significant research in human health. The dataset contains 9130 leaf images (both healthy and unhealthy) from 23 medicinal plant species. These images were collected from various locations in central India. The entire work was carried out over a period of five months, which included plant selection, leaf collection, image capturing, and data organization into folders.

This dataset provides comprehensive information, including the botanical name, common name, geographical origin, healthy and unhealthy leaf images, and medicinal uses of the plants. It serves as a valuable resource for research in machine learning, computer vision, and related domains. Additionally, it will enable the development and evaluation of methodologies for disease detection, plant identification, and other relevant applications.