Project-AgML/tomato_quality_classification

Tomato Quality Classification

A dataset for quality classification of Tomatoes. The dataset contains raw and augmented versions.
The raw dataset contains 1,986 images.
Images per class:

• Fresh: 1,350
• Rotten: 636

The augmented dataset contains 6,000 images.
Images per class:

• Fresh: 3,000
• Rotten: 3,000

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

Citation

@article{khatun2023extensive,
  title={An extensive real-world in field tomato image dataset involving maturity classification and recognition of fresh and defect tomatoes},
  author={Khatun, Tania and Razzak, Abdur and Islam, Md Shofiul and Uddin, Mohammad Shorif},
  journal={Data in Brief},
  volume={51},
  pages={109688},
  year={2023},
  publisher={Elsevier}
}

Khatun, Tania; Razzak, Abdur ; Islam, Md. Shofiul ; Uddin, Prof. Dr. Mohammad Shorif (2023), “Tomato Maturity Detection and Quality Grading Dataset”, Mendeley Data, V1, doi: 10.17632/s42kpg8h37.1

This dataset was reformatted from its original format to match HuggingFace standards.