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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000836 |
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| Summary: | 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. |
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| ISSN: | 2352-3409 |