Automated air-flow cytometry enables real-time monitoring of Plasmopara viticola sporangia in vineyards.

UNLABELLED: Fungal diseases such as downy mildew (Plasmopara viticola) can severely affect grapevine yield and quality, making precise fungicide applications critical. Decision Support Systems (DSS) in precision viticulture guide treatment timing, but most rely primarily on weather data and overlook biological indicators, such as real-time spore dynamics. To address this, we evaluated an automatic air-flow cytometer (SwisensPoleno Jupiter) for real-time detection and quantification of P. viticola sporangia. The instrument integrates holographic imaging, fluorescence spectroscopy, and AI-based classification. Laboratory data sets (>5,000 events) from naturally and artificially infected leaves enabled the development of classifiers based on Random Forest models. Field validation in Swiss vineyards combined Hirst-type spore traps (microscopy and qPCR DNA quantification) with SwisensPoleno measurements. Correlations between manual and automatic sporangium quantification emerged during a 2023 pilot (60 days) and strengthened over the 2024 season (140 days). Correlations were weak early in the season, during leaf development and the onset of flowering, but improved as vine phenology progressed and disease pressure increased, reaching up to r = 0.94 with microscopy counting and r = 0.83 with DNA quantification. Additionally, airborne sporangium concentrations were influenced by daily air temperature and the SwisensPoleno instrument inlet height. Visual assessments in 2024 confirmed severe downy mildew, affecting nearly all unprotected leaves, and P. viticola DNA concentrations were strongly correlated with symptom severity (r = 0.82). With further refinement, these first models for real-time and automatic identification of P. viticola sporangia have the potential to provide reliable spore-monitoring data for DSS integration, supporting precision viticulture worldwide.

IMPORTANCE: The automatic, real-time classification of Plasmopara viticola spores presented in this study offers a significant advancement in disease risk forecasting. By integrating airborne spore detection, this approach reduces the likelihood of false-positive recommendations, applying fungicides when weather conditions are favorable, but disease risk is low and false negatives, when spores are present, but treatments are not advised. Consequently, this strategy can decrease treatment frequency and overall fungicide use, promoting more sustainable and precise disease management in vineyards.