Datasets:
metadata
dataset_info:
- config_name: augmented
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Apple___Apple_scab
'1': Apple___Black_rot
'2': Apple___Cedar_apple_rust
'3': Apple___healthy
'4': Background_without_leaves
'5': Blueberry___healthy
'6': Cherry___Powdery_mildew
'7': Cherry___healthy
'8': Corn___Cercospora_leaf_spot Gray_leaf_spot
'9': Corn___Common_rust
'10': Corn___Northern_Leaf_Blight
'11': Corn___healthy
'12': Grape___Black_rot
'13': Grape___Esca_(Black_Measles)
'14': Grape___Leaf_blight_(Isariopsis_Leaf_Spot)
'15': Grape___healthy
'16': Orange___Haunglongbing_(Citrus_greening)
'17': Peach___Bacterial_spot
'18': Peach___healthy
'19': Pepper,_bell___Bacterial_spot
'20': Pepper,_bell___healthy
'21': Potato___Early_blight
'22': Potato___Late_blight
'23': Potato___healthy
'24': Raspberry___healthy
'25': Soybean___healthy
'26': Squash___Powdery_mildew
'27': Strawberry___Leaf_scorch
'28': Strawberry___healthy
'29': Tomato___Bacterial_spot
'30': Tomato___Early_blight
'31': Tomato___Late_blight
'32': Tomato___Leaf_Mold
'33': Tomato___Septoria_leaf_spot
'34': Tomato___Spider_mites Two-spotted_spider_mite
'35': Tomato___Target_Spot
'36': Tomato___Tomato_Yellow_Leaf_Curl_Virus
'37': Tomato___Tomato_mosaic_virus
'38': Tomato___healthy
splits:
- name: train
num_bytes: 943149189
num_examples: 61486
download_size: 943901022
dataset_size: 943149189
- config_name: raw
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Apple___Apple_scab
'1': Apple___Black_rot
'2': Apple___Cedar_apple_rust
'3': Apple___healthy
'4': Background_without_leaves
'5': Blueberry___healthy
'6': Cherry___Powdery_mildew
'7': Cherry___healthy
'8': Corn___Cercospora_leaf_spot Gray_leaf_spot
'9': Corn___Common_rust
'10': Corn___Northern_Leaf_Blight
'11': Corn___healthy
'12': Grape___Black_rot
'13': Grape___Esca_(Black_Measles)
'14': Grape___Leaf_blight_(Isariopsis_Leaf_Spot)
'15': Grape___healthy
'16': Orange___Haunglongbing_(Citrus_greening)
'17': Peach___Bacterial_spot
'18': Peach___healthy
'19': Pepper,_bell___Bacterial_spot
'20': Pepper,_bell___healthy
'21': Potato___Early_blight
'22': Potato___Late_blight
'23': Potato___healthy
'24': Raspberry___healthy
'25': Soybean___healthy
'26': Squash___Powdery_mildew
'27': Strawberry___Leaf_scorch
'28': Strawberry___healthy
'29': Tomato___Bacterial_spot
'30': Tomato___Early_blight
'31': Tomato___Late_blight
'32': Tomato___Leaf_Mold
'33': Tomato___Septoria_leaf_spot
'34': Tomato___Spider_mites Two-spotted_spider_mite
'35': Tomato___Target_Spot
'36': Tomato___Tomato_Yellow_Leaf_Curl_Virus
'37': Tomato___Tomato_mosaic_virus
'38': Tomato___healthy
splits:
- name: train
num_bytes: 862208169
num_examples: 55448
download_size: 862897905
dataset_size: 862208169
configs:
- config_name: augmented
data_files:
- split: train
path: augmented/train-*
- config_name: raw
data_files:
- split: train
path: raw/train-*
default: true
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 10K<n<100K
Plant Leaf Diseases Dataset Classification
This dataset provides real RGB images of sweet potato leaves affected by various diseases, captured in a controlled laboratory environment in Tainan, Taiwan. Images were acquired using Fujifilm GFX100s and X100F cameras, offering high-resolution visual data for agricultural disease detection research. The dataset contains raw and augmented versions.
The raw dataset contains 55,448 images.
Images per class:
- Apple___Apple_scab: 630
- Apple___Black_rot: 621
- Apple___Cedar_apple_rust: 275
- Apple___healthy: 1,645
- Background_without_leaves: 1,143
- Blueberry___healthy: 1,502
- Cherry___Powdery_mildew: 1,052
- Cherry___healthy: 854
- Corn___Cercospora_leaf_spot Gray_leaf_spot: 513
- Corn___Common_rust: 1,192
- Corn___Northern_Leaf_Blight: 985
- Corn___healthy: 1,162
- Grape___Black_rot: 1,180
- Grape___Esca_(Black_Measles): 1,383
- Grape___Leaf_blight_(Isariopsis_Leaf_Spot): 1,076
- Grape___healthy: 423
- Orange___Haunglongbing_(Citrus_greening): 5,507
- Peach___Bacterial_spot: 2,297
- Peach___healthy: 360
- Pepper,_bell___Bacterial_spot: 997
- Pepper,_bell___healthy: 1,478
- Potato___Early_blight: 1,000
- Potato___Late_blight: 1,000
- Potato___healthy: 152
- Raspberry___healthy: 371
- Soybean___healthy: 5,090
- Squash___Powdery_mildew: 1,835
- Strawberry___Leaf_scorch: 1,109
- Strawberry___healthy: 456
- Tomato___Bacterial_spot: 2,127
- Tomato___Early_blight: 1,000
- Tomato___Late_blight: 1,909
- Tomato___Leaf_Mold: 952
- Tomato___Septoria_leaf_spot: 1,771
- Tomato___Spider_mites Two-spotted_spider_mite: 1,676
- Tomato___Target_Spot: 1,404
- Tomato___Tomato_Yellow_Leaf_Curl_Virus: 5,357
- Tomato___Tomato_mosaic_virus: 373
- Tomato___healthy: 1,591
The augmented dataset contains 61,486 images.
Images per class:
- Apple___Apple_scab: 1,000
- Apple___Black_rot: 1,000
- Apple___Cedar_apple_rust: 1,000
- Apple___healthy: 1,645
- Background_without_leaves: 1,143
- Blueberry___healthy: 1,502
- Cherry___Powdery_mildew: 1,052
- Cherry___healthy: 1,000
- Corn___Cercospora_leaf_spot Gray_leaf_spot: 1,000
- Corn___Common_rust: 1,192
- Corn___Northern_Leaf_Blight: 1,000
- Corn___healthy: 1,162
- Grape___Black_rot: 1,180
- Grape___Esca_(Black_Measles): 1,383
- Grape___Leaf_blight_(Isariopsis_Leaf_Spot): 1,076
- Grape___healthy: 1,000
- Orange___Haunglongbing_(Citrus_greening): 5,507
- Peach___Bacterial_spot: 2,297
- Peach___healthy: 1,000
- Pepper,_bell___Bacterial_spot: 1,000
- Pepper,_bell___healthy: 1,478
- Potato___Early_blight: 1,000
- Potato___Late_blight: 1,000
- Potato___healthy: 1,000
- Raspberry___healthy: 1,000
- Soybean___healthy: 5,090
- Squash___Powdery_mildew: 1,835
- Strawberry___Leaf_scorch: 1,109
- Strawberry___healthy: 1,000
- Tomato___Bacterial_spot: 2,127
- Tomato___Early_blight: 1,000
- Tomato___Late_blight: 1,909
- Tomato___Leaf_Mold: 1,000
- Tomato___Septoria_leaf_spot: 1,771
- Tomato___Spider_mites Two-spotted_spider_mite: 1,676
- Tomato___Target_Spot: 1,404
- Tomato___Tomato_Yellow_Leaf_Curl_Virus: 5,357
- Tomato___Tomato_mosaic_virus: 1,000
- Tomato___healthy: 1,591
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{yang2026label,
title={Label-Free Deep Learning with Feature Adaptation for Crop Anomaly Detection on Small Datasets},
author={Yang, Ming-Der and Lee, Tzu-Han and Tseng, Hsin-Hung and Su, Tung-Ching and Hsu, Yu-Chun},
journal={Agriculture},
volume={16},
pages={854},
year={2026},
publisher={MDPI}
}
The dataset itself can be cited as:
J, A. R. U. N. P. A. N. D. I. A. N. (2019). Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network [Dataset]. Mendeley. https://doi.org/10.17632/TYWBTSJRJV.1
This dataset was reformatted from its original format to match HuggingFace standards.