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Computer-assisted image processing to detect spores from the fungus Pandora neoaphidis.

Reinert Korsnes1, Karin Westrum2, Erling Fløistad2

  • 1Norwegian Defense Research Establishment (FFI), Box 25, N-2027 Kjeller, Norway; Norwegian Institute of Bioeconomy Research (NIBIO), Biotechnology and Plant Health Division, P.O. Box 115, NO-1431 Ås, Norway.

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Summary

This study presents an automated image analysis method for detecting Pandora neoaphidis spores, aiding in biological aphid control and plant protection through aerial spore monitoring.

Keywords:
Biological controlComputerised spore detectionPathogenic fungus

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Area of Science:

  • Mycology
  • Plant Pathology
  • Biological Control

Background:

  • Aerial spore monitoring is crucial for understanding fungal pathogens.
  • Pandora neoaphidis is an entomopathogenic fungus with potential as a biological control agent for aphids.
  • Current methods rely on manual microscopy, which can be labor-intensive.

Purpose of the Study:

  • To develop and demonstrate an experimental automatic image analysis technique for detecting Pandora neoaphidis spores.
  • To support, not replace, human visual inspection of spore imagery.
  • To contribute to more efficient monitoring for plant protection applications.

Main Methods:

  • A workflow involving slide preparation, autonomous image recording, and computerised image analysis was employed.
  • Key image processing steps included segmentation, blob identification, and calculation of blob properties.
  • A novel approach involved projection onto a three-parameter egg shape space for spore characterization.

Main Results:

  • The study demonstrates a feasible method for automatic spore detection.
  • The image analysis supports human inspection by automating initial data processing.
  • The approach provides quantitative data on spore characteristics.

Conclusions:

  • Automated image analysis offers a promising tool for monitoring entomopathogenic fungi like Pandora neoaphidis.
  • This technology can enhance plant protection strategies by improving the efficiency of spore detection.
  • Further development could refine the accuracy and scope of automated fungal spore analysis.