Project-AgML/Luffa_quality_classification

Luffa Quality Classification

This dataset provides real RGB images of luffa plants captured in a field environment in Bangladesh using a handheld smartphone. Collected during October 2023, the images depict natural variations in luffa quality relevant to agricultural disease classification. It serves as a practical resource for developing computer vision models in agricultural quality assessment under real-world field conditions. The dataset contains 343 images across 2 classes: Faulty, Fresh.
Images per class:

• Faulty: 160
• Fresh: 183

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

Citation

@article{sheikh2024luffafolio,
  title={LuffaFolio: A Multidimensional Image Dataset of Smooth Luffa},
  author={Sheikh, Md Ripon and Islam, Md. Masudul and Himel, Galib Muhammad Shahriar},
  journal={Data in Brief},
  volume={53},
  pages={110149},
  year={2024},
  publisher={Elsevier}
}

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