A dataset of annotated African plum images from Cameroon for AI-based quality assessmentKaggle
This paper presents a dataset of 4507 annotated images of African plums collected across diverse regions in Cameroon, marking the first dataset specifically designed for AI-driven quality assessment of this fruit. The dataset is categorized into six quality grades: unaffected, bruised, cracked, rott...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000836 |
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| author | Arnaud Nguembang Fadja Armel Gabin Fameni Tagni Sain Rigobert Che Marcellin Atemkeng |
| author_facet | Arnaud Nguembang Fadja Armel Gabin Fameni Tagni Sain Rigobert Che Marcellin Atemkeng |
| author_sort | Arnaud Nguembang Fadja |
| collection | DOAJ |
| description | This paper presents a dataset of 4507 annotated images of African plums collected across diverse regions in Cameroon, marking the first dataset specifically designed for AI-driven quality assessment of this fruit. The dataset is categorized into six quality grades: unaffected, bruised, cracked, rotten, spotted, and unripe. These categories represent varying degrees of plum quality, from optimal condition to various defects and ripeness levels. Captured under natural lighting using a consistent smartphone setup, the images were meticulously labeled by agricultural experts, ensuring high annotation accuracy. This resource is valuable for developing and testing computer vision, deep learning-based recognition systems and object detection models in agriculture, enabling automated evaluation of plum quality for commercialization. By offering a comprehensive, culturally relevant dataset focused on a traditionally underrepresented crop, this work supports advancements in precision agriculture, particularly in developing regions. Potential applications include AI-based tools for real-time sorting, defect detection, and quality assurance in the supply chain. |
| format | Article |
| id | doaj-art-102d854210a64d37b5d6d65e48d5626b |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-102d854210a64d37b5d6d65e48d5626b2025-02-06T05:12:00ZengElsevierData in Brief2352-34092025-04-0159111351A dataset of annotated African plum images from Cameroon for AI-based quality assessmentKaggleArnaud Nguembang Fadja0Armel Gabin Fameni Tagni1Sain Rigobert Che2Marcellin Atemkeng3Department of Engineering, University of Ferrara, Italy; Corresponding author.College of Technology, University of Buea, PO Box 63, Buea, South West Region, CameroonAfrican Institute for Mathematical Sciences, CameroonDepartment of Mathematics, Rhodes University, South AfricaThis paper presents a dataset of 4507 annotated images of African plums collected across diverse regions in Cameroon, marking the first dataset specifically designed for AI-driven quality assessment of this fruit. The dataset is categorized into six quality grades: unaffected, bruised, cracked, rotten, spotted, and unripe. These categories represent varying degrees of plum quality, from optimal condition to various defects and ripeness levels. Captured under natural lighting using a consistent smartphone setup, the images were meticulously labeled by agricultural experts, ensuring high annotation accuracy. This resource is valuable for developing and testing computer vision, deep learning-based recognition systems and object detection models in agriculture, enabling automated evaluation of plum quality for commercialization. By offering a comprehensive, culturally relevant dataset focused on a traditionally underrepresented crop, this work supports advancements in precision agriculture, particularly in developing regions. Potential applications include AI-based tools for real-time sorting, defect detection, and quality assurance in the supply chain.http://www.sciencedirect.com/science/article/pii/S2352340925000836African plumSafouAgricultural datasetFruit quality assessmentImage classificationObject detection |
| spellingShingle | Arnaud Nguembang Fadja Armel Gabin Fameni Tagni Sain Rigobert Che Marcellin Atemkeng A dataset of annotated African plum images from Cameroon for AI-based quality assessmentKaggle Data in Brief African plum Safou Agricultural dataset Fruit quality assessment Image classification Object detection |
| title | A dataset of annotated African plum images from Cameroon for AI-based quality assessmentKaggle |
| title_full | A dataset of annotated African plum images from Cameroon for AI-based quality assessmentKaggle |
| title_fullStr | A dataset of annotated African plum images from Cameroon for AI-based quality assessmentKaggle |
| title_full_unstemmed | A dataset of annotated African plum images from Cameroon for AI-based quality assessmentKaggle |
| title_short | A dataset of annotated African plum images from Cameroon for AI-based quality assessmentKaggle |
| title_sort | dataset of annotated african plum images from cameroon for ai based quality assessmentkaggle |
| topic | African plum Safou Agricultural dataset Fruit quality assessment Image classification Object detection |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000836 |
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