PRMI: A dataset of minirhizotron images for diverse plant root study - Xu, Weihuang; Yu, Guohao; Cui, Yiming; Gloaguen, Romain; Zare, Alina; Bonnette, Jason; Reyes-Cabrera, Joel; Rajurkar, Ashish; Rowland, Diane; Matamala, Roser; Jastrow, Julie D.; Juenger, Thomas E.; et al. (Dryad, 2021)
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.
This dataset contains over 72 thousand RGB root
images across six different species including cotton, papaya,
peanut, sesame, sunflower, and switchgrass.  In addition, The
images span a variety of conditions including varied root age, root
structures, soil types, and depths under the soil surface. All the images
were collected by Minirhizotron technology. We compiled this dataset using
the raw images directly from the camera sensor. We paired binary
image-level labels for each image showing whether the image contains roots
along with meta-data such as crop species, collection location, MR tube
number, collection time, collection depth, and image resolution. In
addition, over 63 thousand of the images have been manually annotated to
generate pixel-level binary masks indicating whether the pixels correspond
to root or not.
This dataset is mainly used for plant root segmentation tasks. We have
pixel-level annotation (binary masks) for supervised learning and
image-level annotation (whether image contains root) for weakly-supervised
learning.
Keywords: FOS: Agricultural sciences; Computer vision; Transfer learning; weakly-supervised learning; Agricultural science