| --- |
| 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 |
|
|
| ```bibtex |
| @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 |
