Introduction #
The authors of the Maize Whole Plant Image Dataset mentioned the significance of silks in maize, emphasizing their role in pollen collection and grain number determination, particularly under water deficit conditions. They noted that while silk growth is crucial for drought tolerance in maize, phenotyping it efficiently for genetic analyses is challenging.
The authors presented a reproducible pipeline they had developed for tracking ear and silk growth in hundreds of plants daily. This pipeline relied on an ear detection algorithm, which controlled a robotic camera for capturing detailed images of ears and silks. They detailed the six steps involved in the pipeline:
In step 1, the authors acquired RGB color images of each plant from multiple views daily using the PHENOARCH platform.
Step 2 involved image segmentation, where plant pixels were separated from the background using a combination of threshold algorithms.
In step 3, they selected side view images that contained the most information for detecting ear position, considering the visibility of the stem and ear relative to the leaves.
Step 4 focused on the detection of the most probable ear position in the selected side view images, taking into account variations in stem internode width around the ear.
Step 5 explained the process of moving a camera close to the ear once it was detected, involving two imaging cabins and the conversion of pixel positions into [x, y, z] coordinates.
Finally, in step 6, the authors analyzed the images of ears and silks using two different methods, extracting relevant pixel information and computing the time courses of silk growth.
Please note, that authors provided an additional ‘segmentationdata.csv’ file in original data with detailed info such as date, angle, height, sowing, etc.
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Homepage
Research Paper
GitHubSummary #
Maize Whole Plant Image Dataset is a dataset for a semantic segmentation task. It is used in the genetic research.
The dataset consists of 1482 images with 1482 labeled objects belonging to 1 single class (plant).
Images in the Maize Whole Plant Image 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 2017 by the INRAE.
Explore #
Maize Whole Plant Image dataset has 1482 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.
Class balance #
There are 1 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.
Class ㅤ | Images ㅤ | Objects ㅤ | Count on image average | Area on image average |
|---|---|---|---|---|
plant➔ mask | 1482 | 1482 | 1 | 4.08% |
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.
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
plant mask | 1482 | 4.08% | 13.07% | 0.02% | 67px | 3.27% | 2346px | 100% | 1681px | 69.75% | 31px | 1.51% | 1960px | 80.07% |
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.
Objects #
Table contains all 1482 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.
Object ID ㅤ | Class ㅤ | Image name click row to open | Image size height x width | Height ㅤ | Height ㅤ | Width ㅤ | Width ㅤ | Area ㅤ |
|---|---|---|---|---|---|---|---|---|
1➔ | plant mask | plant-1_task-6561_sv180_cabin-1.png | 2448 x 2048 | 296px | 12.09% | 206px | 10.06% | 0.11% |
2➔ | plant mask | plant-1_task-6812_sv330_cabin-1.png | 2448 x 2048 | 2025px | 82.72% | 1074px | 52.44% | 5.19% |
3➔ | plant mask | plant-1_task-6927_sv180_cabin-1.png | 2448 x 2048 | 2341px | 95.63% | 1195px | 58.35% | 3.62% |
4➔ | plant mask | plant-1_task-6899_sv60_cabin-1.png | 2448 x 2048 | 2160px | 88.24% | 1024px | 50% | 4.17% |
5➔ | plant mask | plant-1_task-6926_sv300_cabin-1.png | 2448 x 2048 | 2341px | 95.63% | 1413px | 68.99% | 4.54% |
6➔ | plant mask | plant-1_task-6655_sv0_cabin-2.png | 2448 x 2048 | 872px | 35.62% | 769px | 37.55% | 1.33% |
7➔ | plant mask | plant-1_task-6826_sv30_cabin-1.png | 2448 x 2048 | 2097px | 85.66% | 614px | 29.98% | 4.91% |
8➔ | plant mask | plant-1_task-6603_sv0_cabin-2.png | 2448 x 2048 | 602px | 24.59% | 627px | 30.62% | 0.64% |
9➔ | plant mask | plant-1_task-6619_sv120_cabin-2.png | 2448 x 2048 | 682px | 27.86% | 882px | 43.07% | 0.9% |
10➔ | plant mask | plant-1_task-6619_sv150_cabin-2.png | 2448 x 2048 | 680px | 27.78% | 793px | 38.72% | 0.96% |
License #
Citation #
If you make use of the Maize Whole Plant Image data, please cite the following reference:
@dataset{nicolas_brichet_2017_1002675,
author = {Nicolas BRICHET and
Llorenç CABRERA-BOSQUET},
title = {Maize whole plant image dataset},
month = oct,
year = 2017,
publisher = {Zenodo},
doi = {10.5281/zenodo.1002675},
url = {https://doi.org/10.5281/zenodo.1002675}
}
If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:
@misc{ visualization-tools-for-maize-whole-plant-image-dataset-dataset,
title = { Visualization Tools for Maize Whole Plant Image Dataset },
type = { Computer Vision Tools },
author = { Dataset Ninja },
howpublished = { \url{ https://datasetninja.com/maize-whole-plant-image-dataset } },
url = { https://datasetninja.com/maize-whole-plant-image-dataset },
journal = { Dataset Ninja },
publisher = { Dataset Ninja },
year = { 2024 },
month = { nov },
note = { visited on 2024-11-21 },
}Download #
Dataset Maize Whole Plant Image 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='Maize Whole Plant Image', 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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