Datasets:

Project-AgML
/

PaddyNet_LCC_classification

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': '2'
            '1': '3'
            '2': '4'
            '3': '5'
    - name: image_source
      dtype: string
  splits:
    - name: train
      num_bytes: 48420852
      num_examples: 2785
  download_size: 75551129
  dataset_size: 48420852
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K

Paddynet Lcc Classification

This dataset features field images of paddy crops collected across multiple locations in Bangladesh during the rice growing season (mid-April to late June). Images were captured using handheld RGB cameras on consumer smartphones (Nokia 3 and Samsung S8) and include a mix of real field observations and synthetic augmentations. The dataset contains 2,785 images across 4 classes: 2, 3, 4, 5.
Images per class:

2: 692
3: 1,103
4: 513
5: 477

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

Citation

@article{siddique2023paddynet,
  title={Paddynet: An organized dataset of paddy leaves for a smart fertilizer recommendation system},
  author={Siddique, Md. Moradul and Islam, Torikul and Tusher, Yeasir Arefin and Ema, Romana Rahman and Adnan, Md. Nasim and Galib, Syed Md.},
  journal={Data in Brief},
  volume={50},
  pages={109516},
  year={2023},
  publisher={Elsevier}
}

Siddique, Md Moradul; Islam, Torikul; Tusher, Yeasir Arefin ; Md. Galib, Syed (2023), “PaddyNet: An Organized Dataset of Paddy Leaves for a Smart Fertilizer Recommendation System ”, Mendeley Data, V2, doi: 10.17632/ksz57tk5vc.2

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