Detection of downy mildew infection in grapevine leaves using field spectroscopy and machine learning

Abstract Context Downy mildew, caused by Plasmopara viticola , remains one of the most damaging diseases affecting grapevines, especially in humid viticultural regions such as the “Vinhos Verdes” in northern Portugal. Traditional detection relies on visual inspection and laboratory techniques, which are subjective and reactive, often delaying effective intervention. Aims This study aims to evaluate the potential of field spectroscopy combined with machine learning to detect downy mildew in Vitis vinifera cv. Loureiro field conditions. The focus is on providing an early, non-destructive detection method that can be used in precision viticulture, reducing the need for costly, widespread pesticide applications. Methods and Key Results Leaf spectral reflectance data were collected along the 2023 and 2024 growing seasons using a portable spectroradiometer. Measurements were obtained from both untreated and fungicide-treated grapevines, covering different infection stages. Spectral signatures from 600 grapevine leaves were used to train and validate classification models using Partial Least Squares Linear Discriminant Analysis (PLS-LDA) and Random Forest (RF) models. Both RF and PLS-LDA models showed an overall accuracy of 95.1% when trained with all spectral features from the dataset. Red edge (700–750 nm) and visible (400–700 nm) wavelengths demonstrated the highest classification contribution. Moreover, the twenty most informative wavelengths for infection discrimination were identified for each model. Conclusion The results confirm the effectiveness of field spectroscopy, making it possible to detect downy mildew symptoms in different stages of infection. This method offers a rapid, cost-effective, and sustainable tool for early disease detection, which can greatly benefit winegrowers by enabling more timely and specific interventions and reducing the reliance on chemical treatments. Implications and Impacts This study demonstrates a novel approach to precision viticulture, offering winegrowers a rapid, cost-effective, and sustainable tool for early disease detection. The methodology not only promotes more disease management strategies but also aligns with environmental and regulatory goals.