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）对于包括可持续性与气候适应在内的诸多植物科学研究领域至关重要。微根管（Minirhizotron, MR）技术是一种通过时序采集根系图像以无损表型分析根系构型的常用手段。从微根管图像中精准分割土壤与根系，是研究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图像数据集。该数据集共计包含7.2万余张RGB格式根系图像，涵盖棉花、番木瓜、花生、芝麻、向日葵与柳枝稷共6个物种。图像覆盖多种实验条件，包括不同的根系生长时长、根系结构、土壤类型以及地下采集深度。所有图像均已完成图像级弱标注，用于指示单张图像是否包含根系。此类图像级弱标注可支撑植物根系分割任务中的弱监督学习研究。此外，其中6.3万张图像已完成人工标注，生成了像素级二值掩码，用于指示每个像素是否属于根系。此类像素级二值掩码可作为语义分割任务中监督学习的真值标签。本数据集的发布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研究的发展。