Presymptomatic Leaf Reflectance of Fusarium virguliforme-Infected Soybean Plants in Greenhouse Conditions

Abstract

Sudden death syndrome of soybean is caused by a soilborne pathogen, Fusarium virguliforme. Prior to visible foliar symptoms, a destructive technique can be carried out to diagnose root infection. Hyperspectral sensors can be a presymptomatic and nondestructive alternative for plant disease diagnosis. This study was designed to relate leaf spectral reflectance to F. virguliforme root infection in the absence of foliar symptoms. Soybean plants were grown under controlled greenhouse conditions. The spectral reflectance of the plants was measured weekly beginning at 21 days after transplanting up until 42 days after transplanting using a swing hyperspectral imaging system fixed on a gantry. Destructive root sampling confirmed F. virguliforme root infection using real-time PCR. The most relevant wavelengths for discrimination were selected using the ReliefF algorithm. Three machine learning models (partial least squares discriminant analysis, support vector machine, and random forest) were evaluated for classification accuracy using the selected wavelengths. Relevant wavelengths for differentiating between the healthy and F. virguliforme-infected plants were found in the visible and red-edge region from 500 to 750 nm and the shortwave infrared region from 1,400 to 2,350 nm. In the absence of visible foliar symptoms, classification results showed over 79% mean F1 scores for all models. Partial least squares discriminant analysis was able to differentiate healthy and F. virguliforme-infected plants with a mean F1 score of 83.1 to 85.3% and a kappa statistic of 0.43 to 0.54. This work supports the use of hyperspectral remote sensing for early presymptomatic disease diagnosis under a controlled environment. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .