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Strawberry Disease Detection Dataset

250071864
Tagagriculture
Taskinstance segmentation
Release YearMade in 2021
LicenseCC BY 4.0
Download344 MB

Introduction #

Usman Afzaal, Bhuwan Bhattarai, Yagya Raj Pandeyaet al.

The literature indicates a scarcity of datasets pertaining to the instance segmentation of different kinds of strawberry diseases. Although various models have been developed to perform object detection for multiple diseases in strawberries, there is much to be desired when it comes to datasets allowing fine-grained instance segmentation of multiple diseases and pests in strawberries. In an attempt to fill that void, authors introduce a Strawberry Disease Detection dataset that allows users to segment seven different kinds of strawberry diseases. Since this dataset consists of images that are collected in real fields/green houses instead of a laboratory, it introduces multiple challenges such as having background variations, complex field conditions, different illumination settings, etc. As a result, these variations allows us to design models that have a higher capacity to be more robust and generalizable.

The dataset contains 2500 images for strawberry diseases collected from various greenhouses using camera-equipped mobile phones. The data was collected from multiple greenhouses under natural illumination conditions in South Korea to ensure a diversity of environmental factors. The diseases were verified by experts in the field. Note that approximately 20% of the images contained in the dataset were collected from online sources (Università di Bologna, Bugwood.org (accessed on 22 July 2021); Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA); Nicole Ward Gauthier, University of Kentucky; Gerald Holmes, Strawberry Center, Cal Poly San Luis Obispo, Bugwood.org; William W. Turechek USDA ARS; Frank J. Louws, NC State University; Steven Koike, Plant Pathology Farm Advisor, University of California Agriculture and Natural resources blogs; Garrett Ridge, NC State University; Cornell University; College of Agriculture and Life Science blogs; Madeline Dowling, phytographics.com (accessed on 22 July 2021); Jonas Janner Hamann, Universidade Federal de Santa Maria (UFSM), Bugwood.org; Clemson University—USDA Cooperative Extension Slide Series, Bugwood.org; University of Georgia Plant Pathology, University of Georgia, Bugwood.org; Paul Bachi, University of Kentucky Research and Education Center, Bugwood.org; Scott Bauer, USDA Agricultural Research Service, Bugwood.org; John Hartman, University of Kentucky, Bugwood.org; more details in dataset.txt in original data.). The images in the dataset are processed to be of resolution 419 × 419. With regards to imaging distance, the dataset provides both close-up and distant views of the diseases. The dataset is composed of seven different types of strawberry diseases, with images ranging from initial, middle and final stages of the diseases. The dataset is split into 1450, 307 and 743 images for train, val and test sets, respectively. Table 1 provides a brief summary of this dataset. Online augmentation methods are used and as a result, the final number of images depends on the number of epochs the model is trained on the dataset. The dataset will be made publicly available for further experimentation.

Category of Disease Images for Training Images for Validation Images for Testing
angular_leafspot 245 43 147
anthracnose_fruit_rot 52 12 33
blossom_blight 117 29 62
gray_mold 255 77 145
leaf_spot 382 71 162
powdery_mildew_fruit 80 12 43
powdery_mildew_leaf 319 63 151
Total 1450 307 743

Table 1. A summary of the dataset.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Strawberry Disease Detection is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the agricultural industry, and in the agricultural research.

The dataset consists of 2500 images with 5707 labeled objects belonging to 7 different classes including leaf_spot, powdery_mildew_leaf, gray_mold, and other: angular_leafspot, blossom_blight, powdery_mildew_fruit, and anthracnose_fruit_rot.

Images in the Strawberry Disease Detection dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are 3 splits in the dataset: train (1450 images), test (743 images), and val (307 images). Also, the dataset includes level_1 and level_2 tags for test split. The dataset was released in 2021 by the Jeonbuk National University.

Dataset Poster

Explore #

Strawberry Disease Detection dataset has 2500 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 Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
OpenSample annotation mask from Strawberry Disease DetectionSample image from Strawberry Disease Detection
👀
Have a look at 2500 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 7 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 1-7 of 7
Class
Images
Objects
Count on image
average
Area on image
average
leaf_spot
polygon
615
2100
3.41
61.79%
powdery_mildew_leaf
polygon
533
1708
3.2
26.96%
gray_mold
polygon
476
598
1.26
9.9%
angular_leafspot
polygon
435
539
1.24
56.12%
blossom_blight
polygon
209
300
1.44
3.68%
powdery_mildew_fruit
polygon
141
298
2.11
22.61%
anthracnose_fruit_rot
polygon
100
164
1.64
17.16%

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 1-7 of 7
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
leaf_spot
polygon
2100
17.95%
95.93%
0.4%
33px
7.88%
419px
100%
234px
55.94%
17px
4.06%
419px
100%
powdery_mildew_leaf
polygon
1708
8.3%
75.16%
0.26%
20px
4.77%
419px
100%
160px
38.09%
18px
4.3%
419px
100%
gray_mold
polygon
598
7.73%
72.41%
0.41%
40px
9.55%
419px
100%
166px
39.72%
30px
7.16%
389px
92.84%
angular_leafspot
polygon
539
44.96%
99.52%
0.07%
19px
4.53%
419px
100%
328px
78.27%
16px
3.82%
419px
100%
blossom_blight
polygon
300
2.49%
11.84%
0.67%
50px
11.93%
224px
53.46%
98px
23.44%
32px
7.64%
151px
36.04%
powdery_mildew_fruit
polygon
298
10.59%
62.11%
0.42%
34px
8.11%
419px
100%
168px
40%
28px
6.68%
378px
90.21%
anthracnose_fruit_rot
polygon
164
10.34%
65.14%
0.23%
27px
6.44%
419px
100%
148px
35.28%
23px
5.49%
389px
92.84%

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 5707 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 5707
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
angular_leafspot
polygon
angular_leafspot162.jpg
419 x 419
419px
100%
416px
99.28%
88.98%
2
gray_mold
polygon
gray_mold93.jpg
419 x 419
101px
24.11%
134px
31.98%
4.84%
3
anthracnose_fruit_rot
polygon
anthracnose_fruit_rot36.jpg
419 x 419
84px
20.05%
148px
35.32%
5.62%
4
anthracnose_fruit_rot
polygon
anthracnose_fruit_rot36.jpg
419 x 419
179px
42.72%
243px
58%
15.61%
5
leaf_spot
polygon
leaf_spot64.jpg
419 x 419
393px
93.79%
315px
75.18%
50.27%
6
leaf_spot
polygon
leaf_spot64.jpg
419 x 419
131px
31.26%
111px
26.49%
6.09%
7
leaf_spot
polygon
leaf_spot64.jpg
419 x 419
291px
69.45%
90px
21.48%
12.68%
8
powdery_mildew_leaf
polygon
powdery_mildew_leaf514.jpg
419 x 419
185px
44.15%
296px
70.64%
18.98%
9
angular_leafspot
polygon
angular_leafspot287.jpg
419 x 419
398px
94.99%
330px
78.76%
32.57%
10
anthracnose_fruit_rot
polygon
anthracnose_fruit_rot62.jpg
419 x 419
218px
52.03%
252px
60.14%
23.1%

License #

Strawberry Disease Detection is under CC BY 4.0 license.

Source

Citation #

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

@Article{s21196565,
  AUTHOR = {Afzaal, Usman and Bhattarai, Bhuwan and Pandeya, Yagya Raj and Lee, Joonwhoan},
  TITLE = {An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN},
  JOURNAL = {Sensors},
  VOLUME = {21},
  YEAR = {2021},
  NUMBER = {19},
  ARTICLE-NUMBER = {6565},
  URL = {https://www.mdpi.com/1424-8220/21/19/6565},
  PubMedID = {34640893},
  ISSN = {1424-8220},
  DOI = {10.3390/s21196565}
}

Source

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

@misc{ visualization-tools-for-strawberry-disease-detection-dataset,
  title = { Visualization Tools for Strawberry Disease Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/strawberry-disease-detection } },
  url = { https://datasetninja.com/strawberry-disease-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset Strawberry Disease Detection 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='Strawberry Disease Detection', 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.

. . .

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