Datasets:

Project-AgML
/

tea_leaf_disease_classification

dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': algal_spot
            '1': brown_blight
            '2': gray_blight
            '3': healthy
            '4': helopeltis
            '5': red_spot
  splits:
    - name: train
      num_bytes: 30321493
      num_examples: 5867
  download_size: 31879641
  dataset_size: 30321493
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-nc-4.0
task_categories:
  - image-classification
size_categories:
  - 1K<n<10K

Tea Leaf Disease Classification

A dataset for disease classification of tea leaves. The dataset contains 5,867 images across 6 classes: algal_spot, brown_blight, gray_blight, healthy, helopeltis, red_spot.
Images per class:

algal_spot: 1,000
brown_blight: 867
gray_blight: 1,000
healthy: 1,000
helopeltis: 1,000
red_spot: 1,000

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

Citation

@article{BALASUNDARAM2025103784,
title = {Tea leaf disease detection using segment anything model and deep convolutional neural networks},
journal = {Results in Engineering},
volume = {25},
pages = {103784},
year = {2025},
issn = {2590-1230},
doi = {https://doi.org/10.1016/j.rineng.2024.103784},
url = {https://www.sciencedirect.com/science/article/pii/S2590123024020279},
author = {Ananthakrishnan Balasundaram and Prem Sundaresan and Aryan Bhavsar and Mishti Mattu and Muthu Subash Kavitha and Ayesha Shaik}
}

https://www.kaggle.com/datasets/saikatdatta1994/tea-leaf-disease