PlantDoc - Dataset Ninja

Introduction #

Released 2019-10-16 ·Davinder Singh, Naman Jain, Pranjali Jainet al.

The authors of the PlantDoc: A Dataset for Visual Plant Disease Detection recognized the importance of training models with real-life images to account for the complexities of the real world. In light of this, they decided to create a dataset for accurate plant disease detection in the farm setting by downloading images from Google Images and Ecosia. Collecting large-scale plant disease data through fieldwork would have required significant effort, so they gathered approximately 20,900 images making the final dataset having a total of 27 classes spanning over 13 species with 2,598 images. Numbers in the claimed and the actual dataset differ.

To ensure the quality and relevance of the collected images, four users filtered the images based on their metadata from the websites and guidelines provided on APSNet. The American Phytopathological Society (APS) had compiled a list of peer-reviewed literature corresponding to each plant disease, which the authors referred to in their classification process. Several important factors, such as colour, area, and density of the diseased part, as well as the shape of the species, were considered during the classification process. Inappropriate and duplicate images across classes were removed from the downloaded dataset.

To reduce labeling errors, every image was checked by two individuals following the guidelines. To ensure sufficient training samples, classes with less than 50 images were removed, resulting in a final dataset with 27 classes spanning over 13 species, comprising 2,598 images. Note, that this number of images (taken from the research paper) slightly differs from the actual one on the DatasetNinja (2482 images).

For the object detection task, which required exact bounding regions containing the leaf in each image, the authors used the LabelImg tool to create bounding boxes around the leaves. In cases where the image contained multiple leaves or a combination of diseased and healthy leaves, all the leaves were labeled explicitly with their respective classes. The authors took care to ensure that the entire leaf was encompassed within the bounding box, and that the area of the bounding box was not smaller than approximately 1/8th of the image size. After labeling, the information about the coordinates of the boxes and their respective class labels was stored separately in an XML file corresponding to each image.

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Research Paper

Summary #

PlantDoc: A Dataset for Visual Plant Disease Detection is a dataset for an object detection task. It is used in the agricultural research.

The dataset consists of 2482 images with 8595 labeled objects belonging to 29 different classes including Corn leaf blight, Tomato Septoria leaf spot, Squash Powdery mildew leaf, and other: Raspberry leaf, Potato leaf early blight, Corn rust leaf, Tomato leaf late blight, Peach leaf, Blueberry leaf, Tomato leaf bacterial spot, Potato leaf late blight, Strawberry leaf, Apple Scab Leaf, Apple leaf, Apple rust leaf, Tomato Early blight leaf, Tomato mold leaf, Tomato leaf yellow virus, Tomato leaf, grape leaf, Corn Gray leaf spot, Bell_pepper leaf spot, Soyabean leaf, grape leaf black rot, Bell_pepper leaf, Cherry leaf, Tomato leaf mosaic virus, Potato leaf, and Tomato two spotted spider mites leaf.

Images in the PlantDoc dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train (2251 images) and test (231 images). The dataset was released in 2019 by the Indian Institute of Technology Gandhinagar.

Here is a visualized example for randomly selected sample classes:

Explore #

PlantDoc dataset has 2482 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

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Have a look at 2482 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 29 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-10 of 29

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
Corn leaf blight➔ rectangle	186	357	1.92	67.51%
Tomato Septoria leaf spot➔ rectangle	148	415	2.8	53.39%
Squash Powdery mildew leaf➔ rectangle	128	250	1.95	68.57%
Raspberry leaf➔ rectangle	118	553	4.69	40.47%
Potato leaf early blight➔ rectangle	114	321	2.82	57.51%
Corn rust leaf➔ rectangle	112	123	1.1	77.88%
Tomato leaf late blight➔ rectangle	111	220	1.98	58.87%
Peach leaf➔ rectangle	110	608	5.53	33.98%
Blueberry leaf➔ rectangle	110	777	7.06	41.41%
Tomato leaf bacterial spot➔ rectangle	108	260	2.41	52.13%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-10 of 29

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
Tomato leaf yellow virus rectangle	818	2.45%	18.61%	0.06%	14px	2.25%	1315px	63.13%	121px	15.66%	11px	1.07%	1237px	78.26%
Blueberry leaf rectangle	777	6.38%	86.63%	0.28%	34px	6.54%	1681px	97.34%	264px	24.73%	43px	3.72%	1889px	94.4%
Peach leaf rectangle	608	7.07%	69.05%	0.23%	15px	3%	2908px	99.87%	340px	27.41%	19px	3.03%	1724px	92.46%
Raspberry leaf rectangle	553	9.28%	98.65%	0.54%	41px	6.17%	2490px	99.33%	306px	29.87%	47px	4.79%	3422px	99.92%
Strawberry leaf rectangle	470	10.1%	72.02%	0.67%	55px	6.83%	1673px	98.08%	356px	32.06%	52px	6.02%	1929px	97.36%
Tomato Septoria leaf spot rectangle	415	19.96%	97.35%	0.95%	31px	8.81%	1557px	99.26%	316px	43.57%	17px	7.05%	2475px	99.33%
Tomato leaf rectangle	391	6.31%	95.21%	0.41%	24px	4.63%	2345px	97.9%	311px	23.72%	24px	2.97%	2591px	99.96%
Corn leaf blight rectangle	357	37.07%	96.61%	0.15%	3px	1.05%	5129px	99.93%	509px	62.87%	43px	5.4%	4588px	99.95%
Potato leaf early blight rectangle	321	21.23%	99.49%	0.3%	30px	4.63%	2334px	99.83%	308px	43.3%	26px	4.11%	2999px	99.89%
Bell_pepper leaf rectangle	315	7.83%	66.19%	0.59%	41px	6.47%	1774px	91.07%	246px	26.93%	46px	4.49%	1495px	86.19%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 8595 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 8595

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	Tomato Septoria leaf spot rectangle	7-17-Photo3_Septoria-MARY.jpg	359 x 450	120px	33.43%	50px	11.11%	3.71%
2➔	Tomato Septoria leaf spot rectangle	7-17-Photo3_Septoria-MARY.jpg	359 x 450	279px	77.72%	92px	20.44%	15.89%
3➔	Tomato Septoria leaf spot rectangle	7-17-Photo3_Septoria-MARY.jpg	359 x 450	156px	43.45%	61px	13.56%	5.89%
4➔	Tomato Septoria leaf spot rectangle	7-17-Photo3_Septoria-MARY.jpg	359 x 450	309px	86.07%	210px	46.67%	40.17%
5➔	Apple Scab Leaf rectangle	816.jpg	275 x 330	192px	69.82%	113px	34.24%	23.91%
6➔	Apple Scab Leaf rectangle	816.jpg	275 x 330	189px	68.73%	104px	31.52%	21.66%
7➔	Tomato Early blight leaf rectangle	tomato_early-blight_01_zoom.jpg	378 x 257	196px	51.85%	105px	40.86%	21.18%
8➔	Tomato Early blight leaf rectangle	tomato_early-blight_01_zoom.jpg	378 x 257	195px	51.59%	84px	32.68%	16.86%
9➔	Tomato Early blight leaf rectangle	tomato_early-blight_01_zoom.jpg	378 x 257	176px	46.56%	100px	38.91%	18.12%
10➔	grape leaf black rot rectangle	03gb.jpg	302 x 479	269px	89.07%	295px	61.59%	54.86%

License #

PlantDoc: A Dataset for Visual Plant Disease Detection is under CC BY 4.0 license.

Source

Citation #

If you make use of the PlantDOC data, please cite the following reference:

@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},
  address = {New York, NY, USA},
  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},
  numpages = {5},
  keywords = {Deep Learning, Object Detection, Image Classification},
  location = {Hyderabad, India},
  series = {CoDS COMAD 2020}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-plantdoc-dataset,
  title = { Visualization Tools for PlantDoc Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/plantdoc } },
  url = { https://datasetninja.com/plantdoc },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { may },
  note = { visited on 2024-05-16 },
}

Download #

Dataset PlantDoc can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='PlantDoc', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

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