Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stagesMendeley Data
Machine learning and artificial intelligence have gained widespread popularity across various sectors in Bangladesh, with the notable exception of the agriculture industry. While wealthier nations have extensively adopted machine learning and deep learning techniques in agriculture, Bangladesh'...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925005074 |
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| author | Sayem Kabir Md Fokrul Akon Mohammad Rifat Ahmmad Rashid Maheen Islam Taskeed Jabid Mohammad Manzurul Islam Md Sawkat Ali |
| author_facet | Sayem Kabir Md Fokrul Akon Mohammad Rifat Ahmmad Rashid Maheen Islam Taskeed Jabid Mohammad Manzurul Islam Md Sawkat Ali |
| author_sort | Sayem Kabir |
| collection | DOAJ |
| description | Machine learning and artificial intelligence have gained widespread popularity across various sectors in Bangladesh, with the notable exception of the agriculture industry. While wealthier nations have extensively adopted machine learning and deep learning techniques in agriculture, Bangladesh's agricultural sector has been slower to follow suit. A key factor in the success of any machine learning model is the availability of high-quality datasets. However, practitioners in Bangladesh's mango industry face challenges in leveraging these advanced computational methods due to the lack of standardized and publicly accessible datasets. A well-structured dataset is essential for developing accurate models and reducing misclassification in real-world applications. To address this gap, we have developed a standardized image dataset capturing different stages of mango growth. The dataset, collected between April and June at an orchard on the East West University campus in Bangladesh, consists of 2004 images, each annotated and categorized into four distinct growth stages: early-fruit, premature, mature, and ripe. Although the dataset was created using mangoes from Bangladesh, the growth stages documented are representative of mango development globally, making this dataset applicable to mango cultivation in other countries. The dataset is organized into four folders, each containing both images and corresponding annotation files. We anticipate that this dataset will serve as a valuable resource for researchers and practitioners working in the field of automated agriculture, facilitating the development of machine learning models for monitoring and analyzing mango growth stages. |
| format | Article |
| id | doaj-art-077aad7ba8114a68b2e97e33c591a63b |
| institution | Matheson Library |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-077aad7ba8114a68b2e97e33c591a63b2025-08-04T04:24:15ZengElsevierData in Brief2352-34092025-08-0161111780Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stagesMendeley DataSayem Kabir0Md Fokrul Akon1Mohammad Rifat Ahmmad Rashid2Maheen Islam3Taskeed Jabid4Mohammad Manzurul Islam5Md Sawkat Ali6Department of Computer Science and Engineering, East West University, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka 1212, BangladeshCorresponding author.; Department of Computer Science and Engineering, East West University, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, East West University, Dhaka 1212, BangladeshMachine learning and artificial intelligence have gained widespread popularity across various sectors in Bangladesh, with the notable exception of the agriculture industry. While wealthier nations have extensively adopted machine learning and deep learning techniques in agriculture, Bangladesh's agricultural sector has been slower to follow suit. A key factor in the success of any machine learning model is the availability of high-quality datasets. However, practitioners in Bangladesh's mango industry face challenges in leveraging these advanced computational methods due to the lack of standardized and publicly accessible datasets. A well-structured dataset is essential for developing accurate models and reducing misclassification in real-world applications. To address this gap, we have developed a standardized image dataset capturing different stages of mango growth. The dataset, collected between April and June at an orchard on the East West University campus in Bangladesh, consists of 2004 images, each annotated and categorized into four distinct growth stages: early-fruit, premature, mature, and ripe. Although the dataset was created using mangoes from Bangladesh, the growth stages documented are representative of mango development globally, making this dataset applicable to mango cultivation in other countries. The dataset is organized into four folders, each containing both images and corresponding annotation files. We anticipate that this dataset will serve as a valuable resource for researchers and practitioners working in the field of automated agriculture, facilitating the development of machine learning models for monitoring and analyzing mango growth stages.http://www.sciencedirect.com/science/article/pii/S2352340925005074Mango growth stageAgricultural datasetData preprocessingMachine learningPublic dataset |
| spellingShingle | Sayem Kabir Md Fokrul Akon Mohammad Rifat Ahmmad Rashid Maheen Islam Taskeed Jabid Mohammad Manzurul Islam Md Sawkat Ali Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stagesMendeley Data Data in Brief Mango growth stage Agricultural dataset Data preprocessing Machine learning Public dataset |
| title | Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stagesMendeley Data |
| title_full | Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stagesMendeley Data |
| title_fullStr | Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stagesMendeley Data |
| title_full_unstemmed | Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stagesMendeley Data |
| title_short | Smartphone image dataset for machine learning-based monitoring and analysis of mango growth stagesMendeley Data |
| title_sort | smartphone image dataset for machine learning based monitoring and analysis of mango growth stagesmendeley data |
| topic | Mango growth stage Agricultural dataset Data preprocessing Machine learning Public dataset |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925005074 |
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