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Pebrine diagnosis using quantitative phase imaging and machine learning.

Prasobhkumar P P1, Aravind Venukumar1, Francis C R2

  • 1Department of Instrumentation and Applied Physics, Indian Institute of Science, Bengaluru, India.

Journal of Biophotonics
|May 7, 2021
PubMed
Summary

A new method accurately detects pebrine disease in silkworms using quantitative phase imaging and machine learning. This technique offers a reliable and efficient alternative to traditional microscopic examination for safeguarding sericulture.

Keywords:
HOGMetarhizium anisopliaecell classificationpebrinesericulturetransport of intensity equation

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Area of Science:

  • Sericulture
  • Microbiology
  • Biotechnology

Background:

  • Pebrine is a significant infectious disease impacting silkworm production.
  • Conventional microscopic diagnosis is prone to errors, especially with Metarhizium anisopliae contamination.
  • Accurate pebrine detection is crucial for preventing widespread crop loss.

Purpose of the Study:

  • To develop an efficient and accurate method for detecting pebrine disease in silkworms.
  • To overcome the diagnostic limitations of conventional microscopy.
  • To provide a reliable alternative for pebrine diagnosis in sericulture.

Main Methods:

  • Custom-made motorized brightfield microscope for acquiring focused and defocused images.
  • Quantitative phase imaging using the transport of intensity equation.
  • Machine learning classifier utilizing histogram of oriented gradients features from phase images.

Main Results:

  • The system achieved 97% accuracy in classifying pebrine and Metarhizium anisopliae spores.
  • Classification of spores was performed rapidly at 0.04 seconds per spore.
  • Image acquisition took 2.5 minutes per sample, covering a defined area.

Conclusions:

  • The proposed quantitative phase imaging and machine learning method demonstrates high reliability for pebrine diagnosis.
  • This technique offers a significant improvement over current diagnostic approaches in sericulture.
  • The study presents an efficient alternative for safeguarding silkworm crops from pebrine disease.