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

Version 2.1
HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study
Chang, Spencer J; Chowdhry, Ritesh; Song, Yangyang; Mejia, Tomas; Hampton, Anna; Kucharski, Shelby; Sazzad, TM; Zhang, Yuxuan; Koppal, Sanjeev J; Wilson, Chris H; Gerber, Stefan; Tillman, Barry; Resende Jr., Marcio FR; Hammon, William M; Zare, Alina, 2023, "HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study", https://doi.org/10.7910/DVN/MAYDHT, Harvard Dataverse, V2, UNF:6:XOHEBSkt8rUG72t8Qt6U4g== [fileUNF]
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Description

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
(2023-10-15)
Subject Agricultural Sciences
Keyword root physiology, hyperspectral imagery, minirhizotron, rhizotron, semantic segmentation
Related Publication Spencer J. Chang, Ritesh Chowdhry, Yangyang Song, Tomas Mejia, Anna Hampton, Shelby Kucharski, T.M. Sazzad, Yuxuan Zhang, Sanjeev J. Koppal, Chris H. Wilson, Stefan Gerber, Barry Tillman, Marcio F.R. Resende, William M. Hammond, Alina Zare, HyperPRI: A dataset of hyperspectral images for underground plant root study, Computers and Electronics in Agriculture, Volume 225, 2024, 109307, ISSN 0168-1699,doi: 10.1016/j.compag.2024.109307
Notes Related Publication accepted to Elsevier's Computer and Electronics in Agriculture as of August 2024. Presented through a poster in the CVPPA workshop at ICCV 2023. Related code: https://github.com/GatorSense/HyperPRI.
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