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POSTERS: Disease Detection and Diagnosis I

P-215 - Identifying spectral differences between sugarcane yellow leaf virus-positive and negative samples using hyperspectral imaging

Monday, August 4, 2025
3:45 PM - 4:30 PM HST
Location: Exhibit Hall 3

    Presenting Author(s)

  • GD

    Gustavo Dos Santos

    Louisiana State University
    Baton Rouge, Louisiana, United States

    • Author(s)

  • MR

    Mary-Beth Rollins, n/a

    Reserch Associate
    Department of Plant Pathology and Crop Physiology, Louisiana State University Agricultural Center
    baton Rouge, Louisiana, United States

  • CZ

    Congliang Zhou

    Assistant Professor
    Louisiana State University
    baton Rouge, Louisiana, United States

  • MF

    Madison Flasco, PhD

    Assistant Professor
    Plant Pathology and Crop Physiology, Louisiana State University Agricultural Center
    Baton Rouge, Louisiana, United States

  • FD

    Felipe Dalla Lana

    Assistant Professor
    Louisiana State University
    Rayne, Louisiana, United States

  • AG

    Andre Gama, PhD

    Assistant Professor
    Department of Plant Pathology and Crop Physiology, Louisiana State University Agricultural Center
    baton Rouge, Louisiana, United States

Abstract Text: Sugarcane yellow leaf virus (SCYLV; Polerovirus SCYLV) is a globally widespread pathogen that may significantly reduce yields, leading to economic losses. In Louisiana, surgacane yield losses were reported due to SCYLV, but plants remain asymptomatic, making detection challenging. As part of Louisiana’s propagative material certification, SCYLV incidence is identified through tissue blot immunoassays (TBIA), and fields showing 10% or higher SCYLV incidence are not certified. TBIA is time-consuming and lacks the sensitivity and efficiency needed for real-time monitoring. Therefore, this study developed a hyperspectral imaging approach to improve virus surveillance and prevent selecting heavily infected fields as propagative material. This study analyzes the correlation between hyperspectral signatures of sugarcane leaves and SCYLV infection, confirmed by TBIA. A Python-based hyperspectral processing pipeline automated reflectance computation and spectral standardization. T-test analyses showed significant differences (p < 0.05) at 450 nm (blue), 680 nm (red), and 700–750 nm (red-edge), while wavelengths beyond 750 nm showed non-significant p-values, indicating less spectral differentiation. Future models will integrate molecular assays to enhance detection accuracy and be further validated using additional samples from the Louisiana Seed Cane Certification Program. These findings highlight the potential of hyperspectral analysis for rapid SCYLV detection in Louisiana and globally.