Datasets:
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': grass
'1': white_clover
'2': wild_lettuce
- name: frame_type
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 19485356
num_examples: 5005
download_size: 19428341
dataset_size: 19485356
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: mit
task_categories:
- image-classification
size_categories:
- 1K<n<10K
Grassfield Weed Classification
Grass-field weed patches released with a two-stage weed identification method. Sub-frames cut from images of grass fields are labelled as grass, irregular weed (white clover, Trifolium repens) or regular weed (wild lettuce, Lactuca virosa); the published split has 4,776 sub-frames (3,182 grass, 1,193 clover, 401 lettuce). Larger region frames (229) carry the same three classes and are included too, marked by a frame_type column, with the published train/test split. Images were taken with two cameras under sunny, cloudy and shaded conditions at different locations. The dataset contains 5,005 images across 3 classes: grass, white_clover, wild_lettuce.
Images per class:
- grass: 3,295
- white_clover: 1,275
- wild_lettuce: 435
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
The original train/test/val split has been preserved in the split column.
Citation
@article{calderaracea2024two,
title={A two-stage deep learning strategy for weed identification in grassfields},
author={Calderara-Cea, Felipe and Torres-Torriti, Miguel and Auat Cheein, Fernando and Delpiano, José},
journal={Computers and Electronics in Agriculture},
volume={225},
pages={109300},
year={2024},
publisher={Elsevier}
}
This dataset was reformatted from its original format to match HuggingFace standards.