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A Case Study for Visual Detection of a Systemic Disease: Optimizing Identification of Phony Peach Disease Based on
Weiqi Luo1,2, Kendall A Johnson3, Clive H Bock1,4
1U.S. Department of Agriculture-Agricultural Research Service-U.S. Horticultural Research Laboratory, 2001 South Rock Rd., Fort Pierce, FL 34945, U.S.A.
Phytopathology
|June 22, 2025
Summary
Accurate detection of phony peach disease (PPD) is crucial for orchards. Internode length is the most reliable visual symptom, and deploying two experienced raters improves detection accuracy.
Area of Science:
- Plant Pathology
- Agricultural Science
Background:
- Phony peach disease (PPD), caused by *Xylella fastidiosa* subsp. *multiplex* (Xfm), threatens commercial peach production.
- Current PPD diagnosis relies on visual assessment due to the high cost and logistical challenges of molecular detection (qPCR).
Purpose of the Study:
- To evaluate the accuracy of visual PPD assessment.
- To identify reliable visual symptoms and factors influencing diagnostic accuracy.
- To optimize survey deployment strategies for PPD detection.
Main Methods:
- CART/Random Forest analyses and simulations were used to evaluate visual symptom reliability and rater performance.
- Principal component analysis assessed the impact of rater experience and repeated assessments.
- qPCR was used for pathogen confirmation.
Main Results:
- Internode length was the most reliable visual symptom for PPD identification, outperforming canopy flatness and shape.
- Rater experience and repeated assessments significantly improved agreement with qPCR results.
- Deploying two experienced raters is suggested for optimal survey diagnostic accuracy.
Conclusions:
- Internode length is a key indicator for visual PPD diagnosis.
- Strategic rater deployment and targeted symptom selection enhance disease detection.
- Integrating molecular diagnostics when feasible is recommended for accurate PPD management.
