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Optical sensing method for screening disease in melon seeds by using optical coherence tomography.

Changho Lee1, Seung-Yeol Lee, Jeong-Yeon Kim

  • 1School of Electrical Engineering and Computer Science, Kyungpook National University, 1370, Sankyuk-dong, Buk-gu, Daegu 702-701, Korea. song31037@knu.ac.kr

Sensors (Basel, Switzerland)
|December 14, 2011
PubMed
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Optical coherence tomography (OCT) detects viral infections in melon seeds by identifying unique subsurface layers. This novel optical sensing method aids in the automatic screening of seed abnormalities caused by viruses like Cucumber green mottle mosaic virus (CGMMV).

Area of Science:

  • Plant pathology
  • Biomedical optics
  • Agricultural science

Background:

  • Cucumber green mottle mosaic virus (CGMMV) causes significant damage to melon crops.
  • Accurate and early detection of viral infections in seeds is crucial for agricultural productivity.
  • Existing diagnostic methods may be time-consuming or require specialized laboratory equipment.

Purpose of the Study:

  • To introduce a novel optical sensing method for diagnosing seed abnormalities.
  • To investigate the potential of Optical Coherence Tomography (OCT) for detecting CGMMV infection in melon seeds.
  • To develop and validate a data processing method for OCT analysis of infected seeds.

Main Methods:

  • Melon seeds infected with CGMMV were scanned using Optical Coherence Tomography (OCT).
Keywords:
optical sensingplant imaging

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  • OCT cross-sectional images were analyzed to identify morphological differences between normal and infected seeds.
  • Reverse transcription-polymerase chain reaction (RT-PCR) was used as a confirmatory diagnostic tool.
  • A-scan analysis and 3D OCT image reconstruction were employed for quantitative investigation.

    • Main Results:

    • Abnormal melon seeds infected with CGMMV exhibited an additional subsurface layer not present in normal seeds.
    • OCT successfully identified these distinctive layers in CGMMV-infected seeds.
    • Blind testing (n=140) confirmed the accuracy of OCT in detecting viral presence.
    • Quantitative analysis of abnormal layers (n=40) supported the diagnostic findings.

    Conclusions:

    • OCT, coupled with advanced data processing, can effectively detect morphological changes in seeds induced by viral infections.
    • This optical sensing method offers a promising approach for the systemic and automatic screening of viral infections in seeds.
    • OCT has the potential to become an important tool in agricultural diagnostics for seed health.