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Comparison of Visual and Multispectral Radiometric Disease Evaluations of Cercospora Leaf Spot of Sugar Beet
Author(s) -
K. Steddom,
Mark W. Bredehoeft,
Mohamed F. R. Khan,
C. M. Rush
Publication year - 2005
Publication title -
plant disease
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.663
H-Index - 108
eISSN - 1943-7692
pISSN - 0191-2917
DOI - 10.1094/pd-89-0153
Subject(s) - leaf spot , sugar beet , radiometer , cercospora , multispectral image , biology , reproducibility , remote sensing , botany , agronomy , mathematics , statistics , geography
Visual assessments of disease severity are currently the industry standard for quantification of the necrosis caused by Cercospora beticola on sugar beet (Beta vulgaris) leaves. We compared the precision, reproducibility, and sensitivity of a multispectral radiometer to visual disease assessments. Individual wavebands from the radiometer, as well as vegetative indices calculated from the individual wavebands, were compared with visual disease estimates from two raters at each of two research sites. Visual assessments and radiometric measurements were partially repeated immediately after the first assessment at each site. Precision, as measured by reduced coefficients of variation, was improved for all vegetative indices and individual waveband radiometric measures compared with visual assessments. Visual assessments, near-infrared singlewaveband reflectance values, and four of the six vegetative indices had high treatment F values, suggesting greater sensitivity at discriminating disease severity levels. Reproducibility, as measured by a test-retest method, was high for visual assessments, single-waveband reflectance at 810 nm, and several of the vegetative indices. The use of radiometric methods has the potential to increase the precision of assessments of Cercospora leaf spot foliar symptoms of sugar beet while eliminating potential bias. We recommend this method be used in conjunction with visual disease assessments to improve precision of assessments and guard against potential bias in evaluations.

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