Datasets:
| configs: | |
| - config_name: raw | |
| default: true | |
| data_dir: raw | |
| - config_name: augmented | |
| data_dir: augmented | |
| license: cc-by-4.0 | |
| task_categories: | |
| - image-classification | |
| size_categories: | |
| - 10K<n<100K | |
| dataset_info: | |
| - config_name: augmented | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': Bacterial Spot | |
| '1': Healthy Leaf | |
| '2': Powdery Mildew | |
| '3': Shot Hole | |
| '4': Shot Hole Leaf | |
| '5': Yellow Leaf | |
| - config_name: raw | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': Bacterial Spot | |
| '1': Healthy Leaf | |
| '2': Powdery Mildew | |
| '3': Shot Hole | |
| '4': Shot Hole Leaf | |
| '5': Yellow Leaf | |
| - name: species | |
| dtype: string | |
| # MedLeafX Disease Classification | |
| A dataset for disease classification of 4 medicinal plant species: Camphor, Haritaki, Sojina, and Neem. The dataset contains 10,858 images across 6 classes: Bacterial Spot, Healthy Leaf, Powdery Mildew, Shot Hole, Shot Hole Leaf, Yellow Leaf. | |
| Images per class: | |
| - Bacterial Spot: 2,408 | |
| - Healthy Leaf: 3,497 | |
| - Powdery Mildew: 854 | |
| - Shot Hole: 1,597 | |
| - Shot Hole Leaf: 834 | |
| - Yellow Leaf: 1,668 | |
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. | |
| ## Citation | |
| ```bibtex | |
| @article{ferdous2025ai, | |
| title={AI-MedLeafX: a large-scale computer vision dataset for medicinal plant diagnosis}, | |
| author={Ferdous, Md Fahim and Nissan, Faysal Bin Khaled and Nibir, Nur Muhammad and Bijoy, Md Hasan Imam}, | |
| journal={Data in Brief}, | |
| pages={111945}, | |
| year={2025}, | |
| publisher={Elsevier} | |
| } | |
| ``` | |
| Ferdous, Md. Fahim; Nissan, Faysal Bin Khaled ; Nibir, Nur Muhammad ; Bijoy, Md Hasan Imam (2025), “AI-MedLeafX: A Large-Scale Computer Vision Dataset for Medicinal Plant Diagnosis”, Mendeley Data, V1, doi: 10.17632/zz7r5y4dc6.1 |