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

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 phenotyping relevance

自動化VNIRハイパースペクトル撮像で、発病前のブドウ葉の光学的変化と将来の胞子形成を推定する手法を中心に検証しており、植物病害状態の表現型取得・分類に該当する。

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