metadata
dataset_info:
features:
- name: image
dtype: image
- name: objects
struct:
- name: bbox
list:
list: int64
- name: categories
list:
class_label:
names:
'0': Corn leaf blight
'1': Tomato Early blight leaf
'2': Potato leaf early blight
'3': Potato leaf late blight
'4': Blueberry leaf
'5': grape leaf black rot
'6': Bell_pepper leaf spot
'7': Cherry leaf
'8': Peach leaf
'9': Soyabean leaf
'10': Strawberry leaf
'11': Apple Scab Leaf
'12': Corn rust leaf
'13': Apple leaf
'14': Corn Gray leaf spot
'15': Tomato leaf mosaic virus
'16': Tomato mold leaf
'17': Tomato leaf yellow virus
'18': Tomato leaf bacterial spot
'19': Tomato leaf late blight
'20': Squash Powdery mildew leaf
'21': Bell_pepper leaf
'22': grape leaf
'23': Apple rust leaf
'24': Tomato Septoria leaf spot
'25': Tomato leaf
'26': Raspberry leaf
'27': Potato leaf
'28': Tomato two spotted spider mites leaf
splits:
- name: train
num_bytes: 1027346536
num_examples: 2346
download_size: 975874080
dataset_size: 1027346536
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Plant Doc Detection
A dataset for object detection of various plant leaf diseases. The dataset contains 2,346 images with 8,435 bounding box annotations across 29 categories.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@inproceedings{10.1145/3371158.3371196,
author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},
title = {PlantDoc: A Dataset for Visual Plant Disease Detection},
year = {2020},
isbn = {9781450377386},
publisher = {Association for Computing Machinery},
url = {https://doi.org/10.1145/3371158.3371196},
doi = {10.1145/3371158.3371196},
booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},
pages = {249–253},
series = {CoDS COMAD 2020}
}