Comparing Digital and Visual Evaluations for Accuracy and Precision in Estimating Tall Fescue Brown Patch Severity

Brown patch (Rhizoctonia solani Kuhn), a destructive disease of tall fescue (Festuca arundinacea Schreb.), is typically evaluated visually. The subjectivity of visual evaluations may be reduced using technology like digital image analysis (DIA). This study compared DIA and visual evaluations for accuracy and precision of brown patch ratings of glasshouse grown tall fescue plants. Across four experiments, 112 plants were inoculated with R. solani. Disease was rated visually and using DIA-WP (digital image analysis whole plant canopy). In two experiments, disease evaluations were replicated using three images and three visual evaluations per pot. Absolute error was calculated as the difference between actual disease severity [calculated using an individual leaf DIA method previously quantified as highly predictive of actual brown patch disease severity on tall fescue (r(2) = 0.99)] and DIA-WP and visual evaluations, respectively. Standard deviations within repeated measures were also calculated. A mixed-model ANOVA was used to determine differences (P < 0.05) in mean absolute error and mean standard deviation by method, disease range, and method by disease range. Disease ranged from 0 to 100%. Mean absolute error did not differ between methods but did by disease range, exhibiting a bell-shaped curve from 0% to 100% disease severity. Mean standard deviation exhibited significant method by disease range interaction. Mean standard deviation did not differ across the disease range within DIA-WP evaluations but did across the disease range within visual evaluations. The more consistent precision of DIA across the disease range could reduce variability in brown patch evaluations of tall fescue.