Datasets:

Project-AgML
/

papaya_leaf_disease_detection

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-4.0
task_categories:
  - object-detection
  - image-classification
size_categories:
  - 1K<n<10K
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype: int64
    - name: objects
      struct:
        - name: bbox
          list:
            list: float64
        - name: categories
          list: int64
  splits:
    - name: train
      num_bytes: 119580865
      num_examples: 1050
  download_size: 118561065
  dataset_size: 119580865

Papaya Leaf Disease Detection

A dataset for disease detection of Papaya leaves. The dataset contains 1,050 images with 7,616 bounding box annotations across 5 categories.
The dataset can be used as a classification dataset based on the label column, which contains integer based labels for the following classes:
Anthracnose: 0
Bacterial Spot: 1
Curl: 2
Ring Spot: 3
Healthy: 4

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

Citation

@article{mustofa2024bdpapayaleaf,
  title={BDPapayaLeaf: A dataset of papaya leaf for disease detection, classification, and analysis},
  author={Mustofa, Sumaya and Ahad, Md Taimur and Emon, Yousuf Rayhan and Sarker, Arpita},
  journal={Data in Brief},
  volume={57},
  pages={110910},
  year={2024},
  publisher={Elsevier}
}

Sarker, Arpita ; Mustofa, Sumaya; Ahad, Md Taimur (2024), “BDPapayaLeaf: A annotation based image dataset of papaya leaf disease.”, Mendeley Data, V2, doi: 10.17632/p997fvf526.2