1. Visual Assessment of Phony Peach Disease: Evaluating Rater Accuracy and Reliability.
- Author
-
Johnson KA, Brannen PM, Chen C, and Bock CH
- Subjects
- Reproducibility of Results, Georgia, Plant Diseases microbiology, Xylella genetics, Prunus persica microbiology
- Abstract
Phony peach disease (PPD), found predominantly in central and southern Georgia, is a re-emerging disease caused by Xylella fastidiosa (Xf) subsp. multiplex . Accurate detection and rapid removal of symptomatic trees are crucial to effective disease management. Currently, peach producers rely solely on visual identification of symptoms to confirm PPD, which can be ambiguous if early in development. We compared visual assessment to quantitative PCR (qPCR) for detecting Xf in 'Julyprince' in 2019 and 2020 (JP2019 and JP2020) and in 'Scarletprince' in 2020 (SP2020). With no prior knowledge of qPCR results, all trees in each orchard were assessed by a cohort of five experienced and five inexperienced raters in the morning and afternoon. Visual identification accuracy of PPD was variable, but experienced raters were more accurate when identifying PPD trees. In JP2019, the mean rater accuracy for experienced and inexperienced raters was 0.882 and 0.805, respectively. For JP2020, the mean rater accuracy for experienced and inexperienced raters was 0.914 and 0.816, respectively. For SP2020, the mean rater accuracy for experienced and inexperienced raters was 0.898 and 0.807, respectively. All raters had false positive (FP) and false negative (FN) observations, but experienced raters had significantly lower FN rates compared with the inexperienced group. Almost all raters overestimated the incidence of PPD in the orchards. Reliability of visual assessments was demonstrated as moderate to good, regardless of experience. Further research is needed to develop accurate and reliable methods of detection to aid management of PPD as both FPs and FNs are costly to peach production., Competing Interests: The author(s) declare no conflict of interest.
- Published
- 2024
- Full Text
- View/download PDF