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Makerere University Maize Dataset

158212604
Tagagriculture
Taskobject detection
Release YearMade in 2022
LicenseCC0 1.0
Download12 GB

Introduction #

Released 2022-07-20 ·Claire Babirye, Joyce Nakatumba-Nabende, Gloria Namanyaet al.

The Makerere University Maize Image Dataset was created to provide an open, well-labelled, sufficiently curated and accessible maize image dataset. Data scientists, researchers, and the broader machine learning community can use the dataset for various machine learning experiments to build maize crop disease diagnosis and spatial analysis solutions. The dataset contains maize image data collected across the different regions in Uganda. The dataset has image samples collected from significant maize growing districts selected with the guidance of agricultural experts to obtain a representative dataset.

Note, similar Makerere University Maize Image Dataset datasets are also available on the DatasetNinja.com:

Motivation

While the agricultural sector holds a prominent position in the national economic development agenda of Sub-Saharan Africa, it grapples with challenges posed by crop pests and diseases, particularly affecting crucial food security crops such as Maize. Over the past decade, Maize Leaf Blight disease, also known as Northern Corn Leaf Blight, has emerged as a significant threat in lowland agro-ecological zones. Simultaneously, Maize Streak Disease, caused by the Maize Streak Virus, ranks as the third most serious ailment impacting maize in Sub-Saharan Africa. The prevalence of these diseases has significantly hampered maize yields.

The current approach to data collection and crop pest and disease diagnosis is undergoing a transition from relying on visible symptoms for disease identification to employing data-driven solutions that incorporate machine learning and computer vision techniques. Despite these advancements, the image data previously collected remains inadequately curated, prepared, and shared within the broader community.

Dataset Creation

The dataset was created by scientists from the Makerere Artificial Intelligence Lab and the National Crops Resources Research Institute (NaCRRI) in Namulonge,
Uganda. NaCRRI is an institute of the National Agricultural Research Organisation (NARO) in charge of crop research. Each instance is associated with a class label based on the status of the crop: healthy or diseased. The dataset consists of a crop image with an image status, i.e., healthy, Maize Leaf Blight (mlb), Maize Streak Virus (msv).

image

Maize Data Labels.

The dataset contains maize image data collected across the different regions in Uganda. The dataset has image samples collected from significant maize growing districts selected with the guidance of agricultural experts to obtain a representative dataset. The data consists of raw image data. Each image data point is accompanied with attributes; the crop variety, plant age, district, subcounty, and the datetime of image capture. There are no relationships between the different image instances in the dataset.

Collection Process

The maize image data was collected using mobile phones from farmer gardens. The gardens were identified within the maize growing prominent districts across the four regions in Uganda. The data was collected using the Adsurv application, which is a mobile application that enables crowdsourcing of crop disease data from farmers’ gardens. Adsurv application was installed on mobile phones/tablets used during the data collection process.

Individuals involved in the data collection process included a team of researchers from the Makerere Artificial Intelligence Lab, an agricultural expert affiliated with the cereals program at the National Crops Resources Research Institute (NaCRRI), and a district agricultural officer.

The authors conducted thorough data cleaning procedures, eliminating blurry images, those captured under direct sunlight, and resolving inconsistencies in resolution. In certain instances, data collectors manually input image attribute variables such as crop variety. Modifications to predetermined attributes like districts and sub-counties were made during the data collection phase, leading to inconsistencies in the generated dataset. These inconsistencies manifested as variations in reporting and corrections to district/sub-county information when data had been collected from a different location. The resolution of these inconsistencies was addressed during the subsequent data cleaning process.

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Dataset LinkHomepageDataset LinkDatasheet

Summary #

Makerere University Maize Image Dataset is a dataset for an object detection task. It is used in the agricultural industry.

The dataset consists of 15821 images with 10495 labeled objects belonging to 2 different classes including maize leaf blight and maize streak virus.

Images in the Makerere University Maize dataset have bounding box annotations. There are 5326 (34% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Alternatively, the dataset could be split into 3 health statuses: healthy (5326 images), mlb (5279 images), and msv (5216 images). Additionally, every image marked with the variety, age, district, datetime, subcounty tags. The dataset was released in 2022 by the University of Ghana.

Dataset Poster

Explore #

Makerere University Maize dataset has 15821 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 Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
OpenSample annotation mask from Makerere University MaizeSample image from Makerere University Maize
👀
Have a look at 15821 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 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 1-2 of 2
Class
Images
Objects
Count on image
average
Area on image
average
maize leaf blight
rectangle
5279
5279
1
93.94%
maize streak virus
rectangle
5216
5216
1
94.34%

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 1-2 of 2
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
maize leaf blight
rectangle
5279
93.94%
99.9%
3.46%
82px
16.47%
4031px
100%
869px
95.75%
119px
15.56%
3198px
100%
maize streak virus
rectangle
5216
94.34%
100%
17.36%
268px
32.03%
4028px
100%
895px
96.3%
373px
36.43%
3192px
100%

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 10495 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 10495
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
maize leaf blight
rectangle
1621583746140.jpg
1024 x 1024
1015px
99.12%
1018px
99.41%
98.54%
2
maize streak virus
rectangle
1621420515944.jpg
1024 x 1024
1016px
99.22%
1014px
99.02%
98.25%
3
maize leaf blight
rectangle
1621503689016.jpg
1024 x 1024
659px
64.36%
1018px
99.41%
63.98%
4
maize streak virus
rectangle
1621405420241.jpg
2000 x 3200
1938px
96.9%
3158px
98.69%
95.63%
5
maize leaf blight
rectangle
1621319539757.jpg
2000 x 3200
1984px
99.2%
3186px
99.56%
98.77%
6
maize streak virus
rectangle
1621774487517.jpg
765 x 1024
760px
99.35%
1021px
99.71%
99.06%
7
maize leaf blight
rectangle
1621683245250.jpg
1024 x 1024
1023px
99.9%
1021px
99.71%
99.61%
8
maize leaf blight
rectangle
1621583672860.jpg
1024 x 1024
1020px
99.61%
970px
94.73%
94.36%
9
maize leaf blight
rectangle
1621402504326.jpg
768 x 1024
751px
97.79%
1014px
99.02%
96.83%
10
maize leaf blight
rectangle
1621749048951.jpg
498 x 1024
494px
99.2%
1021px
99.71%
98.91%

License #

Makerere University Maize Image Dataset is under CC0 1.0 license.

Source

Citation #

If you make use of the makerere university maize data, please cite the following reference:

@dataset{makerere university maize,
  author={Babirye, Claire and Nakatumba-Nabende, Joyce and Namanya, Gloria and Mutebi, Chodrine and Ebellu, Moses and Murungi, Joab and Tobius, Saolo and Ssemwogerere, Jonah and Nakayima, Annet and Nabagereka, Deborah and Asasira, Judith and Kanyesigye, Ruth},
  title={Makerere University Maize Image Dataset},
  year={2022},
  url={https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LPGHKK}
}

Source

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

@misc{ visualization-tools-for-makerere-university-maize-dataset,
  title = { Visualization Tools for Makerere University Maize Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/makerere-university-maize } },
  url = { https://datasetninja.com/makerere-university-maize },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset Makerere University Maize 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='Makerere University Maize', 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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