---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': black_nightshade
          '1': cotton
          '2': tomato
          '3': velvet_leaf
  splits:
  - name: train
    num_bytes: 2577776587
    num_examples: 508
  download_size: 2577827161
  dataset_size: 2577776587
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: mit
task_categories:
- image-classification
size_categories:
- n<1K
---
# Crop Weeds Greece

A dataset for image classification of Crop Weeds Greece. The dataset contains 508 images across 4 classes: black_nightshade, cotton, tomato, velvet_leaf.  
Images per class:
- black_nightshade: 123
- cotton: 54
- tomato: 201
- velvet_leaf: 130

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

## Citation

```bibtex
@article{ESPEJOGARCIA2020105306,
title = {Towards weeds identification assistance through transfer learning},
journal = {Computers and Electronics in Agriculture},
volume = {171},
pages = {105306},
year = {2020},
issn = {0168-1699},
doi = {https://doi.org/10.1016/j.compag.2020.105306},
url = {https://www.sciencedirect.com/science/article/pii/S0168169919319854},
author = {Borja Espejo-Garcia and Nikos Mylonas and Loukas Athanasakos and Spyros Fountas and Ioannis Vasilakoglou}
}
```

https://github.com/AUAgroup/early-crop-weed