BDMANGO: An image dataset for identifying the variety of mango based on the mango leavesMendeley Data
In the field of agriculture, particularly within the context of machine learning applications, quality datasets are essential for advancing research and development. To address the challenges of identifying different mango leaf types and recognizing the diverse and unique characteristics of mango va...
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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/S2352340924012034 |
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| author | Mohammad Manzurul Islam Md. Jubayer Ahmed Mahmud Bin Shafi Aritra Das Md. Rakibul Hasan Abdullah Al Rafi Mohammad Rifat Ahmmad Rashid Nishat Tasnim Niloy Md. Sawkat Ali Abdullahi Chowdhury Ahmed Abdal Shafi Rasel |
| author_facet | Mohammad Manzurul Islam Md. Jubayer Ahmed Mahmud Bin Shafi Aritra Das Md. Rakibul Hasan Abdullah Al Rafi Mohammad Rifat Ahmmad Rashid Nishat Tasnim Niloy Md. Sawkat Ali Abdullahi Chowdhury Ahmed Abdal Shafi Rasel |
| author_sort | Mohammad Manzurul Islam |
| collection | DOAJ |
| description | In the field of agriculture, particularly within the context of machine learning applications, quality datasets are essential for advancing research and development. To address the challenges of identifying different mango leaf types and recognizing the diverse and unique characteristics of mango varieties in Bangladesh, a comprehensive and publicly accessible dataset titled “BDMANGO” has been created. This dataset includes images essential for research, featuring six mango varieties: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, and Himsagar, which were collected from different locations. The images were captured using the rear cameras of a Google Pixel 6a and an iPhone XR and were stored in 640 × 480 pixels resolution. Both sides of each mango leaf were photographed against white background to accurately reflect real-world scenarios in mango cultivation fields. The white background was specifically chosen to remove noise in image sample, allowing for accurate feature extraction by machine learning algorithms. This will ensure the trained model's efficacy in identifying a specific mango leaf while implemented alongside any segmentation algorithm. Additionally, image augmentation techniques such as rotation, horizontal flip, vertical flip, width shift, height shift, shear range, and zooming were applied to expand the dataset from 837 original images to a total of 6696 images (837 original image and 5859 augmented images). This expansion significantly enhances the dataset's utility for training, testing, and validating machine learning models designed for classifying mango leaf varieties, thereby supporting research efforts in this domain. |
| format | Article |
| id | doaj-art-6d318a35eccf4eda951d6d0282a49569 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-6d318a35eccf4eda951d6d0282a495692025-01-31T05:11:37ZengElsevierData in Brief2352-34092025-02-0158111241BDMANGO: An image dataset for identifying the variety of mango based on the mango leavesMendeley DataMohammad Manzurul Islam0Md. Jubayer Ahmed1Mahmud Bin Shafi2Aritra Das3Md. Rakibul Hasan4Abdullah Al Rafi5Mohammad Rifat Ahmmad Rashid6Nishat Tasnim Niloy7Md. Sawkat Ali8Abdullahi Chowdhury9Ahmed Abdal Shafi Rasel10Corresponding 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, 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, 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, BangladeshDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, BangladeshIn the field of agriculture, particularly within the context of machine learning applications, quality datasets are essential for advancing research and development. To address the challenges of identifying different mango leaf types and recognizing the diverse and unique characteristics of mango varieties in Bangladesh, a comprehensive and publicly accessible dataset titled “BDMANGO” has been created. This dataset includes images essential for research, featuring six mango varieties: Amrapali, Banana, Chaunsa, Fazli, Haribhanga, and Himsagar, which were collected from different locations. The images were captured using the rear cameras of a Google Pixel 6a and an iPhone XR and were stored in 640 × 480 pixels resolution. Both sides of each mango leaf were photographed against white background to accurately reflect real-world scenarios in mango cultivation fields. The white background was specifically chosen to remove noise in image sample, allowing for accurate feature extraction by machine learning algorithms. This will ensure the trained model's efficacy in identifying a specific mango leaf while implemented alongside any segmentation algorithm. Additionally, image augmentation techniques such as rotation, horizontal flip, vertical flip, width shift, height shift, shear range, and zooming were applied to expand the dataset from 837 original images to a total of 6696 images (837 original image and 5859 augmented images). This expansion significantly enhances the dataset's utility for training, testing, and validating machine learning models designed for classifying mango leaf varieties, thereby supporting research efforts in this domain.http://www.sciencedirect.com/science/article/pii/S2352340924012034Mango leafMango varietyComputer visionArtificial intelligenceImage classification |
| spellingShingle | Mohammad Manzurul Islam Md. Jubayer Ahmed Mahmud Bin Shafi Aritra Das Md. Rakibul Hasan Abdullah Al Rafi Mohammad Rifat Ahmmad Rashid Nishat Tasnim Niloy Md. Sawkat Ali Abdullahi Chowdhury Ahmed Abdal Shafi Rasel BDMANGO: An image dataset for identifying the variety of mango based on the mango leavesMendeley Data Data in Brief Mango leaf Mango variety Computer vision Artificial intelligence Image classification |
| title | BDMANGO: An image dataset for identifying the variety of mango based on the mango leavesMendeley Data |
| title_full | BDMANGO: An image dataset for identifying the variety of mango based on the mango leavesMendeley Data |
| title_fullStr | BDMANGO: An image dataset for identifying the variety of mango based on the mango leavesMendeley Data |
| title_full_unstemmed | BDMANGO: An image dataset for identifying the variety of mango based on the mango leavesMendeley Data |
| title_short | BDMANGO: An image dataset for identifying the variety of mango based on the mango leavesMendeley Data |
| title_sort | bdmango an image dataset for identifying the variety of mango based on the mango leavesmendeley data |
| topic | Mango leaf Mango variety Computer vision Artificial intelligence Image classification |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924012034 |
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