PRMI: A dataset of minirhizotron images for diverse plant root study

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.

解析植物根系构型（Root System Architecture, RSA）对于可持续性、气候适应等诸多植物科学研究领域均具有重要意义。微根管（Minirhizotron，简称MR）技术是目前广泛应用的无损表型分析手段，可通过时序采集根系影像实现植物根系构型的表型鉴定。从微根管影像中精准分割土壤与根系，是解析植物根系构型特征的关键环节。本研究构建了一套基于微根管技术采集的大型植物根系影像数据集：该数据集总计包含超过72000幅RGB根系影像，涵盖棉花、番木瓜、花生、芝麻、向日葵与柳枝稷共6个物种；影像采集场景覆盖多样变量条件，包括不同的根系生长时长、根系结构、土壤类型以及土壤表层下的不同埋设深度。所有影像均已标注图像级弱标签，用于标识该影像是否包含根系，此类标签可支撑植物根系分割任务中的弱监督学习研究。此外，数据集内63000幅影像已完成人工标注，生成了像素级二值掩码，用于标识每个像素是否属于根系区域，此类掩码可作为语义分割任务中监督学习的真值标签。本数据集的构建旨在推动基于深度学习与其他图像分析算法的根系自动分割研究，以及植物根系构型相关的科研工作。

搜集汇总

背景与挑战

背景概述

PRMI数据集是一个包含72,000多张植物根部图像的大规模数据集，涵盖六种不同植物种类，并提供图像级和像素级标注，主要用于植物根部分割研究。