MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment

Kalananthni Pushpanathan, . and Marsyita Hanafi, . and Syamsiah Masohor, . and Wan Fazilah Fazlil Ilahi, . MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment. pp. 413-431. ISSN 2231-8526

Research in the medicinal plants recognition field has received great attention due to the need of producing a reliable and accurate system that can recognise medicinal plants under various imaging conditions. Nevertheless the standard medicinal plant datasets publicly available for research are very limited. This paper proposes a dataset consisting of 34200 images of twelve different high medicinal value local perennial herbs in Malaysia. The images were captured under various imaging conditions such as different scales illuminations and angles. It will enable larger interclass and intraclass variability creating abundant opportunities for new findings in leaf classification. The complexity of the dataset is investigated through automatic classification using several high-performance deep learning algorithms. The experiment results showed that the dataset creates more opportunities for advanced classification research due to the complexity of the images. The dataset can be accessed through https://www.mylpherbs.com/.

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Abstract

Research in the medicinal plants recognition field has received great attention due to the need of producing a reliable and accurate system that can recognise medicinal plants under various imaging conditions. Nevertheless the standard medicinal plant datasets publicly available for research are very limited. This paper proposes a dataset consisting of 34200 images of twelve different high medicinal value local perennial herbs in Malaysia. The images were captured under various imaging conditions such as different scales illuminations and angles. It will enable larger interclass and intraclass variability creating abundant opportunities for new findings in leaf classification. The complexity of the dataset is investigated through automatic classification using several high-performance deep learning algorithms. The experiment results showed that the dataset creates more opportunities for advanced classification research due to the complexity of the images. The dataset can be accessed through https://www.mylpherbs.com/.

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Item Type:	Article
AGROVOC Term:	Data
AGROVOC Term:	Information classification
AGROVOC Term:	Medicinal plants
AGROVOC Term:	Ornamental perennials
AGROVOC Term:	Medicinal herbs
AGROVOC Term:	Digital image processing
AGROVOC Term:	Identification
AGROVOC Term:	knowledge organization system
Depositing User:	Mr. AFANDI ABDUL MALEK
Last Modified:	24 Apr 2025 00:55
URI:	http://webagris.upm.edu.my/id/eprint/10279

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