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RhizoVision Crown: An Integrated Hardware and Software Platform for Root Crown Phenotyping.

Anand SeethepalliHaichao GuoXiuwei LiuMarcus GriffithsHussien AlmtarfiZenglu LiShuyu LiuAlina ZareFelix B FritschiElison B BlancaflorXue-Feng MaLarry M York
Published in: Plant phenomics (Washington, D.C.) (2020)
Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding, genetic mapping, and understanding how roots influence soil resource acquisition. Several imaging protocols and image analysis programs exist, but they are not optimized for high-throughput, repeatable, and robust root crown phenotyping. The RhizoVision Crown platform integrates an imaging unit, image capture software, and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns. The hardware platform utilizes a backlight and a monochrome machine vision camera to capture root crown silhouettes. The RhizoVision Imager and RhizoVision Analyzer are free, open-source software that streamline image capture and image analysis with intuitive graphical user interfaces. The RhizoVision Analyzer was physically validated using copper wire, and features were extensively validated using 10,464 ground-truth simulated images of dicot and monocot root systems. This platform was then used to phenotype soybean and wheat root crowns. A total of 2,799 soybean (Glycine max) root crowns of 187 lines and 1,753 wheat (Triticum aestivum) root crowns of 186 lines were phenotyped. Principal component analysis indicated similar correlations among features in both species. The maximum heritability was 0.74 in soybean and 0.22 in wheat, indicating that differences in species and populations need to be considered. The integrated RhizoVision Crown platform facilitates high-throughput phenotyping of crop root crowns and sets a standard by which open plant phenotyping platforms can be benchmarked.
Keyphrases
  • high throughput
  • high resolution
  • single cell
  • public health
  • machine learning
  • genome wide
  • optical coherence tomography
  • high density
  • data analysis