PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study

Weihuang Xu1         Guohao Yu1         Yiming Cui1         Romain Gloaguen2         Alina Zare1         Jason Bonnette3         Joel Reyes-Cabrera4         Ashish Rajurkar4         Diane Rowland5         Roser Matamala6         Julie D. Jastrow6         Thomas E. Juenger3         Felix B. Fritschi4

1University of Florida 2UniLaSalle Bauvais 3 University of Texas at Austin 4 University of Missouri 5 University of Maine 6 Argonne National Laboratory

AI for Agriculture and Food Systems (AIAFS) in 36th AAAI Conference on Artificial Intelligence 2022

Abstract

Understanding a plant’s root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.

Minirhizotron System


Minirhizotron (MR) technology is one of the few most widely-used approaches for phenotyping RSA non-destructively over time. Generally, before planting, MR transparent tubes are installed in the field at an angle (e.g. 45°) to the soil surface in locations that should eventually be directly under or near to plants of interest. Then, as the plant's root systems grow, a high-resolution camera can be inserted along the tube to capture root images at a variety of depths as shown in the Figure. Since the MR tubes remain in the soil during the entire growing period, the camera is able to capture time-series RGB root images providing insight into RSA development. In addition to the development of the whole root structure, MR imagery can be used to observe changes of roots themselves throughout their life cycle such as color, diameter, angle, and length changes.

Dataset Example

Dataset Summary

Manually Annotated Ground Truth Masks

Materials
Paper
Dataset
Lab

Citation
Plain Text: 
W. Xu, G. Yu, Y. Cui, R. Gloaguen, A. Zare, J. Bonnette, J. Reyes-Cabrera, A. Rajurkar, D. Rowland, R. Matamala, 
J. Jastrow, T. Juenger, and F. Fritschi. “PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study.” 
In AI for Agriculture and Food Systems (AIAFS) Workshops at the AAAI conference on artificial intelligence. 
February, 2022.
BibTex: 
@misc{xu2022prmi,
      title={PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study}, 
      author={Weihuang Xu and Guohao Yu and Yiming Cui and Romain Gloaguen and Alina Zare and Jason Bonnette 
      and Joel Reyes-Cabrera and Ashish Rajurkar and Diane Rowland and Roser Matamala and Julie D. Jastrow 
      and Thomas E. Juenger and Felix B. Fritschi},
      year={2022},
      eprint={2201.08002},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}