BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley Data
Bananas are among the most widely consumed fruits globally due to their appealing flavor, high nutritional value, and ease of digestion. In Bangladesh, bananas hold significant agricultural importance, being one of the most extensively cultivated fruits in terms of land coverage and ranking third in...
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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/S2352340924012010 |
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| author | Md Hasanul Ferdaus Rizvee Hassan Prito Ahmed Abdal Shafi Rasel Masud Ahmed Md. Jahid Hassan Saykot Shanjida Sultan Shanta Sonali Akter Ankan Chandra Das Mohammad Manzurul Islam Mahamudul Hasan Md Sawkat Ali |
| author_facet | Md Hasanul Ferdaus Rizvee Hassan Prito Ahmed Abdal Shafi Rasel Masud Ahmed Md. Jahid Hassan Saykot Shanjida Sultan Shanta Sonali Akter Ankan Chandra Das Mohammad Manzurul Islam Mahamudul Hasan Md Sawkat Ali |
| author_sort | Md Hasanul Ferdaus |
| collection | DOAJ |
| description | Bananas are among the most widely consumed fruits globally due to their appealing flavor, high nutritional value, and ease of digestion. In Bangladesh, bananas hold significant agricultural importance, being one of the most extensively cultivated fruits in terms of land coverage and ranking third in production volume. The banana image dataset presented in this article includes clear and detailed images of four common banana varieties in Bangladesh: Sagor Kola (Musa acuminate), Shabri Kola (Musa sapientum), Bangla Kola (Musa sp.), and Champa Kola (Musa sapientum), as well as four key stages of banana ripeness: Green, Semi-ripe, Ripe, and Overripe. The bananas were collected from wholesale markets and retail fruit shops located in different places in Bangladesh. Overall, the dataset has 2471 original images of different varieties of bananas and 820 original images of varying ripeness stages of bananas. All the images were carefully captured using a high-quality smartphone camera. Later, each image was manually reviewed, maintaining the quality standard throughout the dataset. The augmented version of the banana variety classification dataset contains 7413 images and the augmented banana ripeness stages dataset contains 2457 images. The dataset possesses immense potential in driving innovation and development of automated and efficient processes and mechanisms in several fields, including precision agriculture, food processing, and supply chain management. Machine Learning (ML) and Deep Learning (DL) models can be trained on this dataset to accurately categorize banana varieties and determine their ripeness stages. Such ML and DL models can be leveraged to develop automated systems to determine the optimal harvest time, establish standards for quality control of bananas, develop products and marketing strategies through analysis of consumer preferences for various banana varieties and ripeness levels, and streamline the banana supply chain through improvements in harvesting, sorting, packaging, and inventory management. Additionally, researchers aiming to contribute to developing Computer Vision technologies in food and agricultural sciences will find this dataset valuable in advancing precision farming and food processing mechanisms. Therefore, the dataset has a vast capacity for automating banana production and processing, minimizing the costs of manual labor, and improving overall efficiency. |
| format | Article |
| id | doaj-art-4adc40aa0df946ceb0bcc196b8c16b28 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-4adc40aa0df946ceb0bcc196b8c16b282025-01-31T05:11:37ZengElsevierData in Brief2352-34092025-02-0158111239BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley DataMd Hasanul Ferdaus0Rizvee Hassan Prito1Ahmed Abdal Shafi Rasel2Masud Ahmed3Md. Jahid Hassan Saykot4Shanjida Sultan Shanta5Sonali Akter6Ankan Chandra Das7Mohammad Manzurul Islam8Mahamudul Hasan9Md Sawkat Ali10Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh; Corresponding author.Department of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Agricultural Extension, Ministry of Agriculture, Bogura, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka, BangladeshBananas are among the most widely consumed fruits globally due to their appealing flavor, high nutritional value, and ease of digestion. In Bangladesh, bananas hold significant agricultural importance, being one of the most extensively cultivated fruits in terms of land coverage and ranking third in production volume. The banana image dataset presented in this article includes clear and detailed images of four common banana varieties in Bangladesh: Sagor Kola (Musa acuminate), Shabri Kola (Musa sapientum), Bangla Kola (Musa sp.), and Champa Kola (Musa sapientum), as well as four key stages of banana ripeness: Green, Semi-ripe, Ripe, and Overripe. The bananas were collected from wholesale markets and retail fruit shops located in different places in Bangladesh. Overall, the dataset has 2471 original images of different varieties of bananas and 820 original images of varying ripeness stages of bananas. All the images were carefully captured using a high-quality smartphone camera. Later, each image was manually reviewed, maintaining the quality standard throughout the dataset. The augmented version of the banana variety classification dataset contains 7413 images and the augmented banana ripeness stages dataset contains 2457 images. The dataset possesses immense potential in driving innovation and development of automated and efficient processes and mechanisms in several fields, including precision agriculture, food processing, and supply chain management. Machine Learning (ML) and Deep Learning (DL) models can be trained on this dataset to accurately categorize banana varieties and determine their ripeness stages. Such ML and DL models can be leveraged to develop automated systems to determine the optimal harvest time, establish standards for quality control of bananas, develop products and marketing strategies through analysis of consumer preferences for various banana varieties and ripeness levels, and streamline the banana supply chain through improvements in harvesting, sorting, packaging, and inventory management. Additionally, researchers aiming to contribute to developing Computer Vision technologies in food and agricultural sciences will find this dataset valuable in advancing precision farming and food processing mechanisms. Therefore, the dataset has a vast capacity for automating banana production and processing, minimizing the costs of manual labor, and improving overall efficiency.http://www.sciencedirect.com/science/article/pii/S2352340924012010Machine learningComputer visionImage classificationObject detectionHorticultureFood processing |
| spellingShingle | Md Hasanul Ferdaus Rizvee Hassan Prito Ahmed Abdal Shafi Rasel Masud Ahmed Md. Jahid Hassan Saykot Shanjida Sultan Shanta Sonali Akter Ankan Chandra Das Mohammad Manzurul Islam Mahamudul Hasan Md Sawkat Ali BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley Data Data in Brief Machine learning Computer vision Image classification Object detection Horticulture Food processing |
| title | BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley Data |
| title_full | BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley Data |
| title_fullStr | BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley Data |
| title_full_unstemmed | BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley Data |
| title_short | BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley Data |
| title_sort | bananaimagebd a comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in bangladeshmendeley data |
| topic | Machine learning Computer vision Image classification Object detection Horticulture Food processing |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924012010 |
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