Banana Leaves Imagery Dataset
Abstract In this work, we present a dataset of banana leaf imagery, both with and without diseases. The dataset consists of 11,767 images, categorized as follows: 3,339 healthy images, 3,496 images of leaves affected by Black Sigatoka and 4,932 images of leaves affected by Fusarium Wilt Race 1. This...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04456-4 |
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| _version_ | 1850057609033285632 |
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| author | Neema Mduma Christian Elinisa |
| author_facet | Neema Mduma Christian Elinisa |
| author_sort | Neema Mduma |
| collection | DOAJ |
| description | Abstract In this work, we present a dataset of banana leaf imagery, both with and without diseases. The dataset consists of 11,767 images, categorized as follows: 3,339 healthy images, 3,496 images of leaves affected by Black Sigatoka and 4,932 images of leaves affected by Fusarium Wilt Race 1. This data was collected to support machine learning diagnostics for disease detection. The data collection process involved farmers, researchers, agricultural experts and plant pathologists from the northern and southern highland regions of Tanzania. To ensure unbiased representation, farms were randomly selected from the Rungwe, Mbeya, Arumeru, and Arusha districts, based on the presence of banana crops and the targeted diseases. The dataset offers a comprehensive collection of images captured from November 2022 to January 2023, using a high-resolution smartphone camera across a wide geographical area. Researchers and developers can use this dataset to build machine learning solutions that automatically detect diseases in images, potentially enabling agricultural stakeholders, including farmers, to diagnose Fusarium Wilt Race 1 and Black Sigatoka early and take timely action. |
| format | Article |
| id | doaj-art-0a816c3854564dd1bbb867ec97ed0c79 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-0a816c3854564dd1bbb867ec97ed0c792025-08-20T02:51:23ZengNature PortfolioScientific Data2052-44632025-03-011211510.1038/s41597-025-04456-4Banana Leaves Imagery DatasetNeema Mduma0Christian Elinisa1Nelson Mandela African Institution of Science and Technology, P. O. Box 447Nelson Mandela African Institution of Science and Technology, P. O. Box 447Abstract In this work, we present a dataset of banana leaf imagery, both with and without diseases. The dataset consists of 11,767 images, categorized as follows: 3,339 healthy images, 3,496 images of leaves affected by Black Sigatoka and 4,932 images of leaves affected by Fusarium Wilt Race 1. This data was collected to support machine learning diagnostics for disease detection. The data collection process involved farmers, researchers, agricultural experts and plant pathologists from the northern and southern highland regions of Tanzania. To ensure unbiased representation, farms were randomly selected from the Rungwe, Mbeya, Arumeru, and Arusha districts, based on the presence of banana crops and the targeted diseases. The dataset offers a comprehensive collection of images captured from November 2022 to January 2023, using a high-resolution smartphone camera across a wide geographical area. Researchers and developers can use this dataset to build machine learning solutions that automatically detect diseases in images, potentially enabling agricultural stakeholders, including farmers, to diagnose Fusarium Wilt Race 1 and Black Sigatoka early and take timely action.https://doi.org/10.1038/s41597-025-04456-4 |
| spellingShingle | Neema Mduma Christian Elinisa Banana Leaves Imagery Dataset Scientific Data |
| title | Banana Leaves Imagery Dataset |
| title_full | Banana Leaves Imagery Dataset |
| title_fullStr | Banana Leaves Imagery Dataset |
| title_full_unstemmed | Banana Leaves Imagery Dataset |
| title_short | Banana Leaves Imagery Dataset |
| title_sort | banana leaves imagery dataset |
| url | https://doi.org/10.1038/s41597-025-04456-4 |
| work_keys_str_mv | AT neemamduma bananaleavesimagerydataset AT christianelinisa bananaleavesimagerydataset |