Smartphone image dataset for radish plant leaf disease classification from BangladeshMendeley Data
Radishes, which are common root vegetables, are rich in vitamins and minerals, and contain low calories. This vegetable is known for its rapid growth. Nevertheless, the variety of leaf diseases where leaves get affected by various bacterial and fungal diseases can hinder the healthy growth of radish...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924012253 |
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| author | Mahamudul Hasan Raiyan Gani Mohammad Rifat Ahmmad Rashid Maherun Nessa Isty Raka Kamara Taslima Khan Tarin |
| author_facet | Mahamudul Hasan Raiyan Gani Mohammad Rifat Ahmmad Rashid Maherun Nessa Isty Raka Kamara Taslima Khan Tarin |
| author_sort | Mahamudul Hasan |
| collection | DOAJ |
| description | Radishes, which are common root vegetables, are rich in vitamins and minerals, and contain low calories. This vegetable is known for its rapid growth. Nevertheless, the variety of leaf diseases where leaves get affected by various bacterial and fungal diseases can hinder the healthy growth of radish. Furthermore, there is a high risk of inaccurate identification of diseases if the farmers try to use traditional methods in recognizing these diseases. With the purpose of precise identification of radish leaf diseases for the finest growth of this vegetable, total of 2801 images of the radish leaves are collected from vegetable field in Bangladesh. The collected dataset includes comprehensive images of healthy leaves as well as four types of leaf affected by various diseases such as Black Leaf Spot, Downey Mildew, Flea Beetle and Mosaic. Utilizing this robust dataset, deep learning models can be trained to identify the leaf diseases which helps to detect the diseases in order to reduce the harm of the cultivation of radish. By identifying the diseases on radish leaves accurat-ely and maintaining healthy production of radish, this dataset contributes to the broader sustainability in the agricultural sector. |
| format | Article |
| id | doaj-art-f8083385947946af9173ec4a61a282ed |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-f8083385947946af9173ec4a61a282ed2025-01-31T05:11:44ZengElsevierData in Brief2352-34092025-02-0158111263Smartphone image dataset for radish plant leaf disease classification from BangladeshMendeley DataMahamudul Hasan0Raiyan Gani1Mohammad Rifat Ahmmad Rashid2Maherun Nessa Isty3Raka Kamara4Taslima Khan Tarin5Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshCorresponding author.; Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshRadishes, which are common root vegetables, are rich in vitamins and minerals, and contain low calories. This vegetable is known for its rapid growth. Nevertheless, the variety of leaf diseases where leaves get affected by various bacterial and fungal diseases can hinder the healthy growth of radish. Furthermore, there is a high risk of inaccurate identification of diseases if the farmers try to use traditional methods in recognizing these diseases. With the purpose of precise identification of radish leaf diseases for the finest growth of this vegetable, total of 2801 images of the radish leaves are collected from vegetable field in Bangladesh. The collected dataset includes comprehensive images of healthy leaves as well as four types of leaf affected by various diseases such as Black Leaf Spot, Downey Mildew, Flea Beetle and Mosaic. Utilizing this robust dataset, deep learning models can be trained to identify the leaf diseases which helps to detect the diseases in order to reduce the harm of the cultivation of radish. By identifying the diseases on radish leaves accurat-ely and maintaining healthy production of radish, this dataset contributes to the broader sustainability in the agricultural sector.http://www.sciencedirect.com/science/article/pii/S2352340924012253Disease identificationDataset collectionImage analysisLeaf diseasesAgricultural challengesRadish plant leaf disease |
| spellingShingle | Mahamudul Hasan Raiyan Gani Mohammad Rifat Ahmmad Rashid Maherun Nessa Isty Raka Kamara Taslima Khan Tarin Smartphone image dataset for radish plant leaf disease classification from BangladeshMendeley Data Data in Brief Disease identification Dataset collection Image analysis Leaf diseases Agricultural challenges Radish plant leaf disease |
| title | Smartphone image dataset for radish plant leaf disease classification from BangladeshMendeley Data |
| title_full | Smartphone image dataset for radish plant leaf disease classification from BangladeshMendeley Data |
| title_fullStr | Smartphone image dataset for radish plant leaf disease classification from BangladeshMendeley Data |
| title_full_unstemmed | Smartphone image dataset for radish plant leaf disease classification from BangladeshMendeley Data |
| title_short | Smartphone image dataset for radish plant leaf disease classification from BangladeshMendeley Data |
| title_sort | smartphone image dataset for radish plant leaf disease classification from bangladeshmendeley data |
| topic | Disease identification Dataset collection Image analysis Leaf diseases Agricultural challenges Radish plant leaf disease |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924012253 |
| work_keys_str_mv | AT mahamudulhasan smartphoneimagedatasetforradishplantleafdiseaseclassificationfrombangladeshmendeleydata AT raiyangani smartphoneimagedatasetforradishplantleafdiseaseclassificationfrombangladeshmendeleydata AT mohammadrifatahmmadrashid smartphoneimagedatasetforradishplantleafdiseaseclassificationfrombangladeshmendeleydata AT maherunnessaisty smartphoneimagedatasetforradishplantleafdiseaseclassificationfrombangladeshmendeleydata AT rakakamara smartphoneimagedatasetforradishplantleafdiseaseclassificationfrombangladeshmendeleydata AT taslimakhantarin smartphoneimagedatasetforradishplantleafdiseaseclassificationfrombangladeshmendeleydata |