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Rice Disease Dataset

47031990
Tagagriculture
Taskobject detection
Release YearMade in 2021
LicenseCC0 1.0
Download88 MB

Summary #

Dataset LinkHomepage

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.

Dataset Poster

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.

OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image from Rice Disease
OpenSample annotation mask from Rice DiseaseSample image 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.

Search
Rows 1-3 of 3
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
Bacterial_Blightβž”
rectangle
181
195
1.08
25.05%
Brown_Spotβž”
rectangle
167
1488
8.91
7.25%
Rice_Blastβž”
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 1956
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
Brown_Spot
rectangle
brownspot_orig_021.jpg
300 x 300
55px
18.33%
62px
20.67%
3.79%
2βž”
Brown_Spot
rectangle
brownspot_orig_021.jpg
300 x 300
26px
8.67%
39px
13%
1.13%
3βž”
Rice_Blast
rectangle
blast_rotated_009.jpg
300 x 300
99px
33%
76px
25.33%
8.36%
4βž”
Brown_Spot
rectangle
img_brown_153.jpg
224 x 224
27px
12.05%
14px
6.25%
0.75%
5βž”
Brown_Spot
rectangle
img_brown_12.jpg
425 x 640
15px
3.53%
12px
1.88%
0.07%
6βž”
Brown_Spot
rectangle
img_brown_12.jpg
425 x 640
15px
3.53%
11px
1.72%
0.06%
7βž”
Brown_Spot
rectangle
img_brown_12.jpg
425 x 640
14px
3.29%
10px
1.56%
0.05%
8βž”
Brown_Spot
rectangle
img_brown_12.jpg
425 x 640
10px
2.35%
8px
1.25%
0.03%
9βž”
Brown_Spot
rectangle
img_brown_12.jpg
425 x 640
16px
3.76%
17px
2.66%
0.1%
10βž”
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 = { feb },
  note = { visited on 2024-02-24 },
}

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.

. . .

Disclaimer #

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