Rice Disease - Dataset Ninja

Summary #

Rice Leaf Diseases with Boundary Box is a dataset for an object detection task. Possible applications of the dataset could be in the agricultural industry.

The dataset consists of 470 images with 1956 labeled objects belonging to 3 different classes including Bacterial_Blight, Brown_Spot, and Rice_Blast.

Images in the Rice Disease dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 3 splits in the dataset: train (322 images), valid (87 images), and test (61 images). The dataset was released in 2021.

Explore #

Rice Disease dataset has 470 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.

Sample annotation mask from Rice Disease

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Have a look at 470 images

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

Class balance #

There are 3 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 1-3 of 3

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
Bacterial_Blight➔ rectangle	181	195	1.08	25.05%
Brown_Spot➔ rectangle	167	1488	8.91	7.25%
Rice_Blast➔ rectangle	122	273	2.24	5.48%

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 1-3 of 3

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
Brown_Spot rectangle	1488	0.83%	27.2%	0.02%	6px	1.65%	511px	44.36%	31px	8.06%	6px	1.09%	942px	61.33%
Rice_Blast rectangle	273	2.48%	19.36%	0.09%	20px	3.56%	870px	55.33%	95px	16.02%	20px	2.21%	594px	37%
Bacterial_Blight rectangle	195	23.31%	69.19%	1.39%	14px	2.73%	963px	100%	188px	59.61%	14px	6.25%	3069px	99.61%

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 1956 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 1956

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➔	Brown_Spot rectangle	brownspot_orig_021.jpg	300 x 300	55px	18.33%	62px	20.67%	3.79%
2➔	Brown_Spot rectangle	brownspot_orig_021.jpg	300 x 300	26px	8.67%	39px	13%	1.13%
3➔	Rice_Blast rectangle	blast_rotated_009.jpg	300 x 300	99px	33%	76px	25.33%	8.36%
4➔	Brown_Spot rectangle	img_brown_153.jpg	224 x 224	27px	12.05%	14px	6.25%	0.75%
5➔	Brown_Spot rectangle	img_brown_12.jpg	425 x 640	15px	3.53%	12px	1.88%	0.07%
6➔	Brown_Spot rectangle	img_brown_12.jpg	425 x 640	15px	3.53%	11px	1.72%	0.06%
7➔	Brown_Spot rectangle	img_brown_12.jpg	425 x 640	14px	3.29%	10px	1.56%	0.05%
8➔	Brown_Spot rectangle	img_brown_12.jpg	425 x 640	10px	2.35%	8px	1.25%	0.03%
9➔	Brown_Spot rectangle	img_brown_12.jpg	425 x 640	16px	3.76%	17px	2.66%	0.1%
10➔	Brown_Spot rectangle	img_brown_12.jpg	425 x 640	12px	2.82%	26px	4.06%	0.11%

License #

Rice Leaf Diseases with Boundary Box is under CC0 1.0 license.

Source

Citation #

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

 @misc{nischal lal shrestha_poshan pandey_ashish tiwari_rezina giri_2021,
	title={Rice Disease Dataset},
	url={https://www.kaggle.com/dsv/2481060},
	DOI={10.34740/KAGGLE/DSV/2481060},
	publisher={Kaggle},
	author={Nischal Lal Shrestha and Poshan Pandey and Ashish Tiwari and Rezina Giri},
	year={2021}
}

Source

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

@misc{ visualization-tools-for-rice-disease-dataset,
  title = { Visualization Tools for Rice Disease Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/rice-disease } },
  url = { https://datasetninja.com/rice-disease },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Rice Disease 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='Rice Disease', 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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Disclaimer #

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