We see light differently.

COMPOLYTICS® Macrobot NextGen

Automated multispectral image acquisition system for advanced phenomics (Macroimaging Robot)

Automated sample handling

  • Samples, e.g., detached leaves of crop plants, contained in agar plates (standard microplates) are placed in up to 15 stacks of up to 30 plates each (22 with lids).
  • Plate crane (specialised robot arm) automatically picks plate from stack, places it on linear moving table and removes lid for undisturbed scan.

Multispectral image acquisition

  • Each plate is automatically loaded into imaging box; barcode reader scans sample ID.
  • Customisable sequence of multispectral illumination in UV, VIS, and NIR (tailored to application).
  • Synchronised image acquisition using high-resolution digital camera; background-lit scan for improved object segmentation.

Dedicated user interface

  • Allows complete control of all scan parameters, definition of several scan scenarios including light recipes.
  • Live view of high-resolution camera image plus real-time surveillance image from inside the imaging box.
  • Saving the recorded image data on a local hard drive or in the cloud.

Compatible with various image analysis software

  • Customised proprietary or publicly available image analysis software comprising leaf segmentation, lesion recognition and quantitative assessment.
  • Example shown here: BluVision software for the detection of Blumeria graminis developed at IPK Gatersleben (courtesy of Dimitar Douchkov and Stefanie Lück).

Selected publications from our customers

  • Lück et al., 2020: “Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification, Plant Phenomics 2020(1). DOI:10.34133/2020/5839856
  • Beukert et al., 2021: Efficiency of a Seedling Phenotyping Strategy to Support European Wheat Breeding Focusing on Leaf Rust Resistance, Biology 10(7). DOI:10.3390/biology10070628
  • Rollar et al., 2021: Quantitative Trait Loci Mapping of Adult Plant and Seedling Resistance to Stripe Rust (Puccinia striiformis Westend.) in a Multiparent Advanced Generation Intercross Wheat Population, Front. Plant Sci. 12. DOI:10.3389/fpls.2021.684671
  • Mamun, 2022: The characterization of a peptidase gene (MTA1) strongly associated with powdery mildew resistance in winter wheat, MSc Thesis. DOI:10.13140/RG.2.2.23311.19367
  • Hinterberger et al., 2022: Mining for New Sources of Resistance to Powdery Mildew in Genetic Resources of Winter Wheat, Front. Plant Sci. 13. DOI:10.3389/fpls.2022.836723
  • Sourdille et al., 2022: Crop Improvement: Where Are We Now?, Biology 11(10). DOI:10.3390/biology11101373
  • Hinterberger et al., 2023: High-throughput imaging of powdery mildew resistance of the winter wheat collection hosted at the German Federal ex situ Genebank for Agricultural and Horticultural Crops, GigaScience 12. DOI:10.1093/gigascience/giad007
  • Dracatos et al., 2023: Diversifying Resistance Mechanisms in Cereal Crops Using Microphenomics, Plant Phenomics 5(3). DOI:10.34133/plantphenomics.0023
  • Lück et al., 2020: BluVision Macro – a software for automated powdery mildew and rust disease quantification on detached leaves, The Journal of Open Source Software 5(51):2259. DOI:10.21105/joss.02259

Please send us your Macrobot related publication to get it listed here.

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