DIRS Laboratory 76-3215
December 5, 2019 at 3:00pm
MS Thesis Defense



Sclerotinia sclerotiorum, or white mold, is a fungus that infects the flowers of snap bean plants and causes a subsequent reduction in snap bean pods, which adversely impacts yield. Timing the application of white mold fungicide thus is essential to preventing the disease, and is most effective when applied during the flowering stage. However, most of the flowers are located beneath the canopy, i.e., hidden by foliage, which makes spectral detection of flowering via the leaf/canopy spectra paramount. The overarching objectives of this research therefore are to i) identify spectral signatures for the onset of flowering to optimally time the application of fungicide, ii) investigate spectral characteristics prior to white mold onset in snap beans, and iii) eventually link the location of white mold with biophysical (spectral and structural) metrics to create a spatially-explicit probabilistic risk model for the appearance of white mold in snap bean fields. To find pure vegetation pixels in the canopy of the flowering beans toward creating the discriminating and predictive models, spectral angle mapper (SAM) and ratio and thresholding (RT) were used. Average reflective power (ARP), on the other hand, was used to find pure pixels in regions of interest that contained mold to establish the mold models. The pure pixels then were used with a single feature logistic regression (SFLR) to identify wavelengths, spectral ratio indices, and normalized difference indices that best separated the flowering and mold classes. Features with the largest c-index were used to train a support vector machine (SVM) and applied to imagery from a different growing season to evaluate model robustness. This research found that single wavelength features in the near-infrared’s red edge region discriminated and predicted flowering up to two weeks before visible flowering, with c-index values above 90%. However, it was found that canopy-level discrimination of snap beans diseased with white mold was not possible using these methods. Therefore, a spectra-to-LAI regression was needed to predict mold occurrence in the canopy using ground truth LAI measurements.