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RoCoLe Dataset

156071
Tagagriculture
Tasksemantic segmentation
Release YearMade in 2019
LicenseCC BY 4.0
Download664 MB

Introduction #

Released 2019-05-17 ·Jorge Parraga-Alava, Kevin Cusme, Angélica Looret al.

The authors present RoCoLe: A Robusta Coffee Leaf Images Dataset. It comprises 1560 leaf images that exhibit visible red mites and spots to indicate the presence of coffee leaf rust in infection cases. For healthy cases, the dataset includes images without such structures. The dataset also contains annotations describing objects (leaves), their state (healthy or unhealthy), and the severity of disease based on the leaf area with spots. All images were captured under real-world conditions in the same coffee plant field using a smartphone camera. The RoCoLe dataset serves as a valuable resource for assessing the performance of machine learning algorithms in image segmentation and classification tasks related to plant disease recognition.

RoCoLe offers a collection of images in binary and multiclass classification problems, as well as segmentation tasks related to plant disease recognition. This dataset encompasses images of both the upper and back sides of coffee leaves collected from robusta coffee crops, representing various states (healthy and unhealthy), the presence of diseases (rust and red_spider_mite), and varying levels of infection severity. Specifically, there are four images available for each of the 390 coffee plants included in the study, resulting in a total of 1560 robusta coffee leaf images in the dataset.

Experimental Design, Materials, and Methods

The images of healthy and unhealthy coffee plant leaves were captured using a 5-MP smartphone camera at a working distance of 200-300 mm, without employing zoom. These images were taken under diverse real-world conditions, including variations in lighting (cloudy, sunny, and windy days), backgrounds (other plants and weeds), and temperature levels (high and low humidity). This diversity ensures that the dataset includes real and representative samples of coffee plants. The study area featured a crop with 390 coffee plants, and for each coffee plant, images of both the upper and back sides of healthy and infected leaves were obtained, resulting in the collection of 1560 images in total.

To enhance RoCoLe’s capabilities for evaluating machine learning methods, a comprehensive annotation process was conducted to label all 1560 coffee leaf images. This annotation process was carried out using a Labelbox and encompassed two types of annotations: object segmentation and classification.

The RoCoLe dataset features annotations for object segmentation, where each object (leaf) is recognized and segmented. Additionally, each object is classified as healthy, exhibiting red mite presence, or manifesting rust at various severity levels, namely rust_level_1, rust_level_2, rust_level_3, and rust_level_4. The determination of rust infection severity was based on the OIRSA method, summarized in the following table:

Level Affected leaf area (spots)
1 1–5%
2 6–20%
3 21–50%
4 >50%
ExpandExpand
Dataset LinkHomepageDataset LinkResearch Paper

Summary #

RoCoLe: A Robusta Coffee Leaf Images Dataset is a dataset for a semantic segmentation task. It is used in the agricultural and biological research.

The dataset consists of 1560 images with 2329 labeled objects belonging to 7 different classes including healthy, unhealthy, rust_level_1, and other: red_spider_mite, rust_level_2, rust_level_3, and rust_level_4.

Images in the RoCoLe dataset have pixel-level semantic segmentation annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2019 by the Escuela Superior Politecnica Agropecuaria de Manabi, Equador and Universidad de Santiago de Chile.

Here is the visualized example grid with animated annotations:

Explore #

RoCoLe dataset has 1560 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 RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
OpenSample annotation mask from RoCoLeSample image from RoCoLe
👀
Have a look at 1560 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
healthy
polygon
791
791
1
24.49%
unhealthy
polygon
769
769
1
27.53%
rust_level_1
polygon
344
344
1
27.63%
red_spider_mite
polygon
167
167
1
24.64%
rust_level_2
polygon
166
166
1
29.12%
rust_level_3
polygon
62
62
1
29.68%
rust_level_4
polygon
30
30
1
28.84%

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
healthy
polygon
791
24.4%
58.01%
6.06%
198px
22.91%
2205px
94.96%
603px
49.64%
568px
16.76%
4058px
100%
unhealthy
polygon
769
27.45%
55.37%
7.79%
202px
22.95%
1926px
99.58%
678px
55.39%
436px
33.14%
4091px
100%
rust_level_1
polygon
344
27.54%
55.37%
8.5%
202px
28.06%
1818px
95.75%
627px
53.61%
556px
33.89%
4091px
100%
red_spider_mite
polygon
167
24.58%
53.9%
7.79%
273px
25.88%
1926px
93.84%
813px
54.7%
750px
33.14%
3757px
100%
rust_level_2
polygon
166
29.04%
53.45%
10.59%
229px
22.95%
1510px
94.58%
637px
57.76%
616px
36.58%
3619px
100%
rust_level_3
polygon
62
29.6%
50.13%
9.37%
271px
33.81%
1245px
99.58%
684px
58.26%
436px
34.06%
2997px
99.66%
rust_level_4
polygon
30
28.77%
47.56%
13.92%
322px
43.8%
1202px
95.69%
758px
60.89%
654px
47.48%
3204px
95.39%

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 2329 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 2329
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
healthy
polygon
cjvqu5atniqn50804yaf615x2.jpeg
1152 x 2048
545px
47.31%
1552px
75.78%
23.01%
2
unhealthy
polygon
cjvnypugkphy10804bi8r0iea.jpeg
2322 x 4128
1064px
45.82%
2445px
59.23%
17.77%
3
red_spider_mite
polygon
cjvnypugkphy10804bi8r0iea.jpeg
2322 x 4128
1064px
45.82%
2445px
59.23%
17.77%
4
unhealthy
polygon
cjvqrdazphxgb0a08yzfef0ib.jpeg
720 x 1280
343px
47.64%
1074px
83.91%
26.08%
5
rust_level_2
polygon
cjvqrdazphxgb0a08yzfef0ib.jpeg
720 x 1280
343px
47.64%
1074px
83.91%
26.08%
6
unhealthy
polygon
cjvnf8dd6fz1s0828a8zwknn1.jpeg
1152 x 2048
524px
45.49%
1388px
67.77%
18.89%
7
rust_level_2
polygon
cjvnf8dd6fz1s0828a8zwknn1.jpeg
1152 x 2048
524px
45.49%
1388px
67.77%
18.89%
8
unhealthy
polygon
cjvqw64e3j3tr0828u6quizmg.jpeg
1152 x 2048
508px
44.1%
1890px
92.29%
26.56%
9
rust_level_2
polygon
cjvqw64e3j3tr0828u6quizmg.jpeg
1152 x 2048
508px
44.1%
1890px
92.29%
26.56%
10
unhealthy
polygon
cjvqtum7cinqh080417541ev4.jpeg
720 x 1280
587px
81.53%
622px
48.59%
22.28%

License #

RoCoLe: A Robusta Coffee Leaf Images Dataset is under CC BY 4.0 license.

Source

Citation #

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

Parraga-Alava, Jorge; Cusme, Kevin; Loor, Angélica; Santander, Esneider (2019), “RoCoLe: A robusta coffee leaf images dataset ”, Mendeley Data, V2, doi: 10.17632/c5yvn32dzg.2

Source

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

@misc{ visualization-tools-for-rocole-dataset,
  title = { Visualization Tools for RoCoLe Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/rocole } },
  url = { https://datasetninja.com/rocole },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset RoCoLe 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='RoCoLe', 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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