Early Warning of Cucumber Angular Leaf Spot by Estimating Airborne Pathogen Aerosols with Particulate Matter Sensors

Airborne bacterial diseases driven by pathogen aerosols in enclosed greenhouses spread rapidly, challenging traditional early-warning methods. This study developed a two-step monitoring system for cucumber angular leaf spot using low-cost particulate matter (PM) sensors, qPCR, and machine learning. Evaluated across spatially independent greenhouse trials using 732 plot-days of data, PM sensors were utilized as dynamic physical proxies alongside microclimate data. These proxies continuously estimated the fluctuations of pathogen aerosols suspended in the greenhouse air. When estimated aerosol risks exceeded a pathogenic threshold, targeted air sampling and qPCR quantification were triggered. For pathogen monitoring, the Extra Trees (ET) surveillance model accurately predicted the accumulation of airborne pathogen aerosols (R2 = 0.884). For disease forecasting, by integrating the quantified aerosol loads with environmental factors, the XGBoost prediction model forecasted the daily disease index change rate with high precision (R2 = 0.874). SHapley Additive exPlanations (SHAP) analysis confirmed that the concentration of airborne pathogen aerosols and vapor pressure deficit were primary drivers of disease expansion. By combining continuous physical sensing of greenhouse air with risk-triggered biological quantification, this framework provides a feasible strategy to partly compensate for the lack of biological specificity of PM sensors and supports early-warning management of airborne bacterial diseases in protected agriculture.