REMP: A unique dataset of rare and endangered medicinal plants in Bangladesh for sustainable healing and biodiversity conservation

In Bangladesh, there are significant number of medicinal plants, but currently no comprehensive record of these valuable species is publicly available. Alarmingly, some of these plants are in a precarious state of endangerment. Therefore, we are creating a unique dataset of Bangladeshʼs rare, endang...

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Bibliographic Details
Published in:	Data in brief Vol. 57; p. 110895
Main Authors:	Islam, Mohammad Manzurul, Rahman, Sanjida, Hoque, Nahida, Mamun, Md. Al, Moheuddin, Md. Sultan, Ali, Md. Sawkat, Rashid, Mohammad Rifat Ahmmad, Masum, Saleh, Ferdaus, Md. Hasanul, Niloy, Nishat Tasnim, Rahman, Md. Atiqur
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier Inc 01-12-2024 Elsevier
Subjects:	Data Endangered plants Machine learning Medicinal plants Rare plants Threatened plants Medicinal plants Endangered plants Threatened plants Rare plants Machine learning
Online Access:	Get full text
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Summary:	In Bangladesh, there are significant number of medicinal plants, but currently no comprehensive record of these valuable species is publicly available. Alarmingly, some of these plants are in a precarious state of endangerment. Therefore, we are creating a unique dataset of Bangladeshʼs rare, endangered, and threatened medicinal plants to support conservation efforts. It will help us to track and conserve endangered plant species, ensuring a more organized approach to research and preservation efforts. We conducted on-site visits to the National Botanical Garden and The Government Unani and Ayurvedic Medical College, capturing photographs of these plants in optimal sunlight conditions at various times of the day. This involved fieldwork, detailed image annotations, dataset organization, diversity augmentation, and contribution to the preservation of our natural heritage. We have collected a total of 16 types of rare and endangered medicinal plant leaf photos to create our unique dataset consisting of a total of 3494 images. This dataset will help researchers in biodiversity conservation through building efficient machine learning models and applying advanced machine learning techniques to identify rare and endangered medicinal plants.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2352-3409 2352-3409
DOI:	10.1016/j.dib.2024.110895