MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment
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 v...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universiti Putra Malaysia Press
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/98160/1/23%20JST-2755-2021 |
| _version_ | 1825937999360163840 |
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| author | Pushpanathan, Kalananthni Hanafi, Marsyita Mashohor, Syamsiah Fazlil Ilahi, Wan Fazilah |
| author_facet | Pushpanathan, Kalananthni Hanafi, Marsyita Mashohor, Syamsiah Fazlil Ilahi, Wan Fazilah |
| author_sort | Pushpanathan, Kalananthni |
| collection | UPM |
| description | 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/. |
| first_indexed | 2024-03-06T11:07:54Z |
| format | Article |
| id | upm.eprints-98160 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T11:07:54Z |
| publishDate | 2022 |
| publisher | Universiti Putra Malaysia Press |
| record_format | dspace |
| spelling | upm.eprints-981602022-08-13T00:38:14Z http://psasir.upm.edu.my/id/eprint/98160/ MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment Pushpanathan, Kalananthni Hanafi, Marsyita Mashohor, Syamsiah Fazlil Ilahi, Wan Fazilah 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/. Universiti Putra Malaysia Press 2022 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/98160/1/23%20JST-2755-2021 Pushpanathan, Kalananthni and Hanafi, Marsyita and Mashohor, Syamsiah and Fazlil Ilahi, Wan Fazilah (2022) MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment. Pertanika Journal of Science & Technology, 30 (1). pp. 413-431. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-2755-2021 10.47836/pjst.30.1.23 |
| spellingShingle | Pushpanathan, Kalananthni Hanafi, Marsyita Mashohor, Syamsiah Fazlil Ilahi, Wan Fazilah MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment |
| title | MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment |
| title_full | MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment |
| title_fullStr | MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment |
| title_full_unstemmed | MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment |
| title_short | MYLPHerb-1: a dataset of Malaysian local perennial herbs for the study of plant images classification under uncontrolled environment |
| title_sort | mylpherb 1 a dataset of malaysian local perennial herbs for the study of plant images classification under uncontrolled environment |
| url | http://psasir.upm.edu.my/id/eprint/98160/1/23%20JST-2755-2021 |
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