Project-AgML/FruitVision_quality_classification

FruitVision Quality Classification

A dataset for quality classification of apples, bananas, mangoes, grapes, and oranges. The dataset contains raw and augmented versions.
The raw dataset contains 10,154 images.
Images per class:

• Formalin-mixed: 3,176
• Fresh: 3,800
• Rotten: 3,178

The augmented dataset contains 73,389 images.
Images per class:

• Formalin-mixed: 22,228
• Fresh: 30,400
• Rotten: 20,761

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

Citation

@article{bijoy2025fruitvision,
  title={FruitVision: A benchmark dataset for fresh, rotten, and formalin-mixed fruit detection},
  author={Bijoy, Md Hasan Imam and Tasnim, Syeda Zarin and Awsaf, Syed Ali and Hasan, Md Zahid},
  journal={Data in Brief},
  volume={61},
  pages={111752},
  year={2025},
  publisher={Elsevier}
}

Bijoy, Md Hasan Imam; Tasnim, Syeda Zarin; Awsaf, Syed Ali; Hasan, Md Zahid (2025), “FruitVision: A Benchmark Dataset for Fresh, Rotten, and Formalin-mixed Fruit Detection”, Mendeley Data, V2, doi: 10.17632/xkbjx8959c.2