Assessing the capacity of high-resolution commercial satellite imagery for grapevine downy mildew detection and surveillance in New York state

Abstract

Grapevine downy mildew (GDM), caused by the oomycete Plasmopara viticola, can cause 100% yield loss and vine death under conducive conditions. Growers currently rely on frequent fungicide applications for control, but this practice has led to widespread resistance. Rapid remote detection and surveillance of GDM outbreaks would enable precision pesticide applications to target effective but resistance-prone fungicides where and when most needed, while relying on less resistance-prone protectants elsewhere. High resolution commercial satellite platforms offer the opportunity to track rapidly spreading diseases like GDM over large, heterogeneous fields. Here, we investigate the capacity of PlanetScope (3 m) and SkySat (50 cm) imagery for season-long GDM detection and surveillance. A team of trained scouts rated GDM severity and incidence in two acres of Chardonnay grapevines in Geneva, NY, USA in June-August of 2020, 2021, and 2022. Satellite imagery acquired within 72 hours of scouting was processed to extract single-band reflectance and vegetation indices (VIs). Random forest models trained on spectral bands and VIs derived from both image datasets could classify areas of high and low GDM incidence and severity with maximum accuracies of 0.88 (SkySat) and 0.94 (PlanetScope). However, we do not observe significant differences between VIs of high and low damage classes until late July-early August. We identify cloud cover, image co-registration, and low spectral resolution as key challenges to operationalizing satellite-based GDM surveillance. This work establishes the capacity of spaceborne multispectral sensors to detect late-stage GDM and outlines steps towards incorporating satellite remote sensing in grapevine disease surveillance systems.

Plant phenotyping relevance

衛星画像と機械学習を用いてブドウのべと病の発生・重症度を直接推定し、精度評価と運用上の課題を検証しているため、植物病害フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe satellite imagery analysis, disease ratings, and random forest modeling, but contain no public dataset deposit, no author code repository, and no data availability statement with a URL. Satellite imagery is commercial (PlanetScope/SkySat), and no paper-specific public assets are identified.

No evidence-backed public reproduction asset is currently recorded.