Datasets:
metadata
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
@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}
}