Datasets:

Project-AgML
/

sugarcane_damage_usa

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': cracked
            '1': crushed
            '2': no_buds
            '3': no_damage
            '4': single_damaged_buds
            '5': two_buds
  splits:
    - name: train
      num_bytes: 2301208454
      num_examples: 153
  download_size: 2301235850
  dataset_size: 2301208454
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
task_categories:
  - image-classification
size_categories:
  - n<1K

Sugarcane Damage Usa

A dataset for classification of sugarcane damage. The dataset contains 153 images across 6 classes: cracked, crushed, no_buds, no_damage, single_damaged_buds, two_buds.
Images per class:

cracked: 24
crushed: 24
no_buds: 21
no_damage: 36
single_damaged_buds: 24
two_buds: 24

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

Citation

@ARTICLE{8412587,
author={Alencastre-Miranda, Moises and Davidson, Joseph R. and Johnson, Richard M. and Waguespack, Herman and Krebs, Hermano Igo},
journal={IEEE Robotics and Automation Letters},
title={Robotics for Sugarcane Cultivation: Analysis of Billet Quality using Computer Vision},
year={2018},
volume={3},
number={4},
pages={3828-3835},
doi={10.1109/LRA.2018.2856999}
}