Automated, high-throughput hyperspectral imaging enables early detection of grapevine downy mildew and monitoring of vineyard spray program performance

Grapevine downy mildew (GDM), caused by Plasmopara viticola, is managed largely through repeated fungicide applications, yet evaluating spray-program performance is difficult because field infections are spatially heterogeneous. However, host physiological changes can precede visible symptom development. We tested whether standardized, high-throughput VNIR hyperspectral imaging of field-grown grapevine leaf discs can capture early optical changes associated with infection and discriminate program-linked mitigation. Leaves were collected from a 2025 vineyard trial in Geneva, NY (cv. La Crescent) managed under three spray programs (conventional fungicides, biofungicides, and untreated control). Leaf discs (≈87 per program) were excised, inoculated with P. viticola, and imaged at 0.5, 1.5, and 3.5 days post inoculation (dpi) using a custom built, automated, hyperspectral imaging microscopy platform (400-980 nm). No visible sporulation occurred at 0.5 or 1.5 dpi; sporulation was first observed at 3.5 dpi and occurred only in the untreated control, whereas no discs from either treated program sporulated. Multivariate spectral analyses showed significant separation by spray program and dpi, and disease-related spectral indices exhibited program-dependent trajectories, with treated discs showing attenuated optical change relative to the control. A random-forest classifier trained on pre-sporulation spectra (0.5 and 1.5 dpi; N = 169) predicted subsequent sporulation with 0.78 accuracy and 0.77 ROC-AUC (95% CI 0.68-0.85), highlighting informative bands across visible, red-edge, and near-infrared regions. Together, these results support outcome-linked, replicate-rich hyperspectral phenotyping of vineyard spray programs using field-derived material under standardized acquisition conditions.

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Publication Details

Journal
Plant Disease
Published
2026-06-29
DOI
https://doi.org/10.1094/pdis-03-26-0540-sr
Primary Topic
Horticultural and Viticultural Research
Type
article
Field-Weighted Citation Impact
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article

Automated, high-throughput hyperspectral imaging enables early detection of grapevine downy mildew and monitoring of vineyard spray program performance

Anna Underhill, Lorenzo Cotrozzi, C H Chen, Jinhong Yu et al.
Plant Disease
Horticultural and Viticultural Research
article

Automated, high-throughput hyperspectral imaging enables early detection of grapevine downy mildew and monitoring of vineyard spray program performance

Anna Underhill, Lorenzo Cotrozzi, C H Chen, Jinhong Yu, Yi Jiang, KF Gold, Rachel Weber, David Combs, Lorenzo Pippi, Nicolaas Regnier, Aliyah Brewer, Lance Cadle-Davidson
article en

Abstract

Grapevine downy mildew (GDM), caused by Plasmopara viticola, is managed largely through repeated fungicide applications, yet evaluating spray-program performance is difficult because field infections are spatially heterogeneous. However, host physiological changes can precede visible symptom development. We tested whether standardized, high-throughput VNIR hyperspectral imaging of field-grown grapevine leaf discs can capture early optical changes associated with infection and discriminate program-linked mitigation. Leaves were collected from a 2025 vineyard trial in Geneva, NY (cv. La Crescent) managed under three spray programs (conventional fungicides, biofungicides, and untreated control). Leaf discs (≈87 per program) were excised, inoculated with P. viticola, and imaged at 0.5, 1.5, and 3.5 days post inoculation (dpi) using a custom built, automated, hyperspectral imaging microscopy platform (400-980 nm). No visible sporulation occurred at 0.5 or 1.5 dpi; sporulation was first observed at 3.5 dpi and occurred only in the untreated control, whereas no discs from either treated program sporulated. Multivariate spectral analyses showed significant separation by spray program and dpi, and disease-related spectral indices exhibited program-dependent trajectories, with treated discs showing attenuated optical change relative to the control. A random-forest classifier trained on pre-sporulation spectra (0.5 and 1.5 dpi; N = 169) predicted subsequent sporulation with 0.78 accuracy and 0.77 ROC-AUC (95% CI 0.68-0.85), highlighting informative bands across visible, red-edge, and near-infrared regions. Together, these results support outcome-linked, replicate-rich hyperspectral phenotyping of vineyard spray programs using field-derived material under standardized acquisition conditions.

Plant Disease
University of Pisa (IT), Agricultural Research Service (US), Scuola Superiore Sant'Anna (IT), Cornell University (US), Klaipėda University (LT), New York State College of Agriculture & Life Sciences (US)
Reduced inequalities
Openalex Percentile: Top 9%
Horticultural and Viticultural Research
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