Project-AgML/tuta_absoluta_tomato_damage_classification

Tuta Absoluta Tomato Damage Classification

Canopy images of tomato plants grown in net-house plots in Arusha, Tanzania, between August 2018 and May 2019, taken during the two weeks after Tuta absoluta (tomato leaf miner) larvae were inoculated at the early growth stage, released with a segmentation-based method for quantifying Tuta absoluta damage. This dataset holds the 4,341 images the record labels in file_labels.csv as healthy (not inoculated) or Tuta absoluta affected; the record's two collections (dataset 1 and dataset 2) are kept as a column. The dataset contains 4,341 images across 2 classes: healthy, tuta_absoluta_affected.
Images per class:

• healthy: 2,336
• tuta_absoluta_affected: 2,005

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

Citation

@article{loyani2024segmentation,
  title={Segmentation-based quantification of Tuta absoluta’s damage on tomato plants},
  author={Loyani, Loyani},
  journal={Smart Agricultural Technology},
  volume={7},
  pages={100415},
  year={2024},
  publisher={Elsevier}
}

The dataset itself can be cited as:

Rubanga, D. P., Mkonyi, L., Mgaya Richard, Never Zekeya, Loyani K. Loyani, Sawahiko Shimada, Machuve, D., & Baraka Maiseli. (2020). A Deep Learning Dataset for Tomato Pest Leafminer TUTA ABSOLUTA (Version V1) [Graphic]. Zenodo. https://doi.org/10.5281/ZENODO.4305416

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