Abstract

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. Spectral angle mapper (SAM) and ratio and thresholding (RT) were used to detect pure vegetation pixels, toward creating the flowering detection 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 classes. Features with the largest c-index were used to train a support vector machine (SVM) and were then applied to imagery from a different growing season to evaluate model robustness. This research found that single wavelength features in the red (600-700 nm, with a peak at 680 nm) discriminated and predicted flowering up to two weeks before visible flowering occurred, with c-index values above 90%. Structural metrics, such as leaf area index (LAI), have been proven to correlate with white mold incidence, so linear and multivariate regressions were used to ingest spatial- and spectral-related features, derived from the imaging spectroscopy data, and predict ground truth LAI data. These features included raw spectral reflectance values, pixel density, normalized difference index (NDVI), green normalized difference index (GNDVI), and the enhanced vegetation index (EVI). Indicators in the green and red-red edge portion of the spectrum exhibited coefficients of determination (CoD) greater than 0.7. The spatial and spectral indices had CoDs and root mean squared errors (RMSE) ranging from 0.422-0.565 and 0.817-0.942, respectively. The top 28 features were used in a multivariate regression to predict LAI and the results showed a maximum adjusted CoD of 0.849, with an RMSE of 0.390. Future work should include raw reflectance values, LAI correlated spectral features, as well as auxiliary in-field measurements (degree days, average rainfall, average temperature) in the creation of a white mold risk model.

Library of Congress Subject Headings

Green bean--Diseases--Remote sensing; Sclerotinia sclerotiorum--Remote sensing

Publication Date

12-2019

Document Type

Thesis

Student Type

Graduate

Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)

Advisor

Jan van Aardt

Advisor/Committee Member

Carl Salvaggio

Advisor/Committee Member

Emmett Ientilucci

Comments

This thesis has been embargoed. The full-text will be available on or around 12/20/2020.

Campus

RIT – Main Campus

Plan Codes

IMGS-MS

Available for download on Saturday, December 19, 2020

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