Dataset Ninja LogoDataset Ninja:

DiaMOS Plant Dataset

35055649
Tagagriculture
Taskobject detection
Release YearMade in 2021
LicenseCC BY 4.0
Download10 GB

Introduction #

Released 2021-10-08 ·Gianni Fenu, Francesca Maridina Malloci

The authors of the DiaMOS Plant: A Dataset for Diagnosis and Monitoring Plant Disease contribute to the evolving field of foliar disease classification and recognition through the utilization of machine and deep learning concepts. It has 3505 images of pear fruit and leaves affected by four diseases: slug leaf, spot leaf, curl leaf, and healthy leaf. The study offers valuable guidelines for the research community to select and construct further datasets.

The direct visual examination of leaves serves as a crucial source of information for assessing plant health. Leaf symptoms are early indicators of various diseases, infections, parasites, and deficiencies that impact plant development and life cycles. Biotic and abiotic stresses, significant factors limiting agricultural productivity, necessitate innovative and sustainable cultivation practices.

DiaMOS Plant is introduced as a field dataset for diagnosing and monitoring plant symptoms. It covers an entire growing season of a pear tree, containing 3505 images depicting four leaf stresses and three stages of fruit development. The dataset, suitable for machine and deep learning methods, includes detailed information on fruit and leaf images, resolution variations, and acquisition devices.

DiaMOS Plant Dataset
Plant Pear
Cultivar Septoria Piricola
Data Source Location Sardegna, Italy
Type of Data RGB Images
ROI (Region of Interest) Leaf, Fruit
Total Size 3505 images

Dataset description

The authors of the dataset comprise images captured using different devices, including smartphones and DSLR cameras. Two resolutions, 2976 × 3968 and 3456 × 5184, add complexity and value to the dataset. Multiple devices were employed to simulate real-world scenarios where operators have diverse technical characteristics in their devices. Leaves were captured from the adaxial side under various realistic conditions, including different lighting, angles, backgrounds, and noise levels. This approach allowed for the representation of leaves under diverse lighting conditions and the observation of symptom evolution over time.

Smartphone Camera DSRL Camera
Image size 2976 × 3968 3456 × 5184
Model device Honor 6× Canon EOS 60D
Focal length 3.83 mm 50 mm
Focal ratio f/2.2 f/4.5
Color space RGB RGB

Dataset annotation

The dataset labeling process involved manual annotation using the LabelImg software. An expert assisted in disease recognition, and severity levels were assigned for specific classes. Severity levels ranged from no risk (0%) to high risk (>50%), providing detailed information on the affected leaf area.
The dataset’s representativeness, complexity, and detailed annotations contribute to its potential for advancing research in the field of plant symptom diagnosis and monitoring. Some annotations are of poor quality: the bounding boxes do not correspond to the location of objects in the image.

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Dataset LinkHomepageDataset LinkResearch Paper 1 (main)Dataset LinkResearch Paper 2Dataset LinkGitHub

Summary #

DiaMOS Plant: A Dataset for Diagnosis and Monitoring Plant Disease is a dataset for an object detection task. It is used in the agricultural industry.

The dataset consists of 3505 images with 3753 labeled objects belonging to 5 different classes including slug leaf, spot leaf, pear, and other: curl leaf and healthy leaf.

Images in the DiaMOS Plant dataset have bounding box annotations. There are 13 (0% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Additionally, the images have severity level and source tags. The severity level is determined based on the percentage of leaf area affected, source - identifies the device with which the image was taken. The dataset was released in 2021 by the University of Cagliari, Italy.

Dataset Poster

Explore #

DiaMOS Plant dataset has 3505 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 DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
OpenSample annotation mask from DiaMOS PlantSample image from DiaMOS Plant
👀
Have a look at 3505 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 5 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 1-5 of 5
Class
Images
Objects
Count on image
average
Area on image
average
slug leaf
rectangle
2025
2028
1
26.69%
spot leaf
rectangle
884
885
1
26.73%
pear
rectangle
486
734
1.51
14.73%
curl leaf
rectangle
54
63
1.17
20.28%
healthy leaf
rectangle
43
43
1
27.87%

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 1-5 of 5
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
slug leaf
rectangle
2028
26.7%
55.62%
8.01%
864px
26.94%
3343px
84.25%
2009px
52.27%
732px
24.6%
3082px
91.7%
spot leaf
rectangle
885
26.73%
73.33%
4.12%
836px
24.14%
3773px
95.09%
2077px
53.09%
582px
14.67%
3882px
77.45%
pear
rectangle
734
10.29%
31.07%
0%
7px
0.18%
3069px
74.71%
1326px
37.27%
27px
0.91%
2992px
59.88%
curl leaf
rectangle
63
17.47%
44.51%
3.76%
574px
16.61%
4160px
80.25%
1860px
46.4%
607px
11.98%
3292px
63.69%
healthy leaf
rectangle
43
27.87%
58.77%
9.51%
1275px
33.87%
3122px
79.64%
2144px
55.38%
811px
27.25%
2597px
74.7%

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 3753 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 3753
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
slug leaf
rectangle
u1711.jpg
3968 x 2976
2401px
60.51%
1666px
55.98%
33.87%
2
curl leaf
rectangle
u3117.jpg
3456 x 5184
1775px
51.36%
1828px
35.26%
18.11%
3
slug leaf
rectangle
u1623.jpg
3968 x 2976
1801px
45.39%
1549px
52.05%
23.62%
4
slug leaf
rectangle
u1519.jpg
3968 x 2976
1891px
47.66%
1406px
47.24%
22.52%
5
slug leaf
rectangle
u1478.jpg
3968 x 2976
2487px
62.68%
1220px
40.99%
25.69%
6
slug leaf
rectangle
u2286.jpg
3968 x 2976
2340px
58.97%
1657px
55.68%
32.83%
7
pear
rectangle
234.jpg
3456 x 5184
2137px
61.83%
1129px
21.78%
13.47%
8
slug leaf
rectangle
u2417.jpg
3968 x 2976
2375px
59.85%
1641px
55.14%
33%
9
slug leaf
rectangle
u1782.jpg
3968 x 2976
2367px
59.65%
1224px
41.13%
24.53%
10
spot leaf
rectangle
u2118.jpg
3968 x 2976
2406px
60.64%
1453px
48.82%
29.6%

License #

A Dataset for Diagnosis and Monitoring Plant Disease is under CC BY 4.0 license.

Source

Citation #

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

@dataset{gianni_fenu_2021_5557313,
  author       = {Gianni Fenu and
                  Francesca Maridina Malloci},
  title        = {{DiaMOS Plant Dataset: A Dataset for Diagnosis and
                   Monitoring Plant Disease}},
  month        = oct,
  year         = 2021,
  publisher    = {Zenodo},
  version      = 1,
  doi          = {10.5281/zenodo.5557313},
  url          = {https://doi.org/10.5281/zenodo.5557313}
}

Source

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

@misc{ visualization-tools-for-diamos-plant-dataset,
  title = { Visualization Tools for DiaMOS Plant Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/diamos-plant } },
  url = { https://datasetninja.com/diamos-plant },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset DiaMOS Plant 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='DiaMOS Plant', 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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