Using Artificial intelligence for automatic and fast detection of downy mildew symptoms in grapevine canopies

Abstract

Downy mildew is one of the most destructive diseases in wine-growing regions, severely reducing yield and fruit quality. Traditional detection methods rely on expert scouting, which is labour-intensive, subjective and often imprecise. In this context, Artificial intelligence (AI) offers a promising alternative, enabling the use of conventional RGB images for field level disease detection. Therefore, this study proposes a deep learning-based approach to identify leaves with downy mildew symptoms using canopy-level RGB imagery. A comprehensive dataset was generated from fourteen commercial blocks with varying cultivars and disease intensities in northern Spain. RGB images of the canopy were collected under different lighting conditions throughout the day to increase the dataset variability. The YOLOv4 (You Only Look Once) algorithm was trained using this heterogeneous dataset to enhance robustness of the model. The model achieved a mAP of 67 %, an F1-score of 0.69 and an IoU of 62 % in the testing process applied on full canopy images. The number of infected leaves predicted by the model closely matched expert annotation reaching a determination coefficient R² of 0.93. Detection performance remained consistent across different infection levels, suggesting the model’s adaptability to a wide range of conditions. Furthermore, the model was able to accurately localise symptomatic leaves within the full canopy. The results of this study demonstrate that RGB images of the whole canopies can be effectively used to detect downy mildew symptoms, offering a practical approach for in-field disease monitoring. The proposed methodology facilitates the development of automated, on-the-go disease detection systems using mobile platforms such as agricultural robots or vehicles, thereby enabling real-time crop health assessment.

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