HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study

Here we present Hyperspectral Plant Root Imagery (HyperPRI), the first available dataset of RGB and HSI data for in situ, non-destructive, underground plant root analysis using machine learning tools. HyperPRI contains images of plant roots grown in rhizoboxes for two annual crop species – peanut (Arachis hypogaea) and sweet corn (Zea mays). Drought conditions are simulated once, and the boxes are imaged and weighed on select days across two months. Along with the images, we provide hand-labeled semantic masks and imaging environment metadata. HyperPRI may be applied to semantic segmentation, plant phenotyping, and drought resilience studies. The proposed dataset may also have transferable insights for other datasets containing thin object features among highly textured backgrounds. Dataset Features Red-green-blue (RGB) and hyperspectral imaging (HSI) data Temporal data for rhizoboxes - plants are monitored from seedling till they are reproductively mature. Thin roots as narrow as 1-3 pixels Highly texture soil background High-resolution spectral data with high correlation between channels Computer Vision Tasks Compute root characteristics (length, diameter, angle, count, system architecture, hyperspectral) Determine root turnover Observe drought resiliency and response Compare multiple physical and hyperspectral plant traits across time Investigate texture analysis techniques Segment roots vs. soil

本研究发布高光谱植物根系影像数据集（Hyperspectral Plant Root Imagery，缩写HyperPRI），这是首个可结合机器学习工具开展原位、无损地下植物根系分析的红-绿-蓝 (RGB) 与高光谱成像 (HSI) 数据集。HyperPRI收录了两种一年生作物——花生（Arachis hypogaea）与甜玉米（Zea mays）在根盒 (rhizobox) 中培育的根系影像。研究单次模拟干旱胁迫，随后在两个月内的选定日期对根盒进行成像与称重。除影像数据外，本数据集还附带人工标注的语义掩码 (semantic masks) 与成像环境元数据 (metadata)。该数据集可应用于语义分割、植物表型分析与干旱抗性研究。此外，本数据集对于包含高纹理背景下细目标特征的其他数据集，同样具备可迁移的研究参考价值。
数据集特征：
- 红-绿-蓝 (RGB) 与高光谱成像 (HSI) 数据
- 根盒时序数据：从幼苗期直至生殖成熟阶段全程监测植株生长
- 可捕捉窄至1-3像素的细根系
- 高纹理土壤背景
- 通道间相关性较高的高分辨率光谱数据
计算机视觉任务：
- 计算根系特征（长度、直径、角度、数量、根系构型与高光谱特征）
- 测定根系周转
- 观测植株干旱抗性与响应模式
- 跨时间维度比对多种物理与高光谱植物性状
- 研究纹理分析技术
- 实现根系与土壤的语义分割