Downy mildew and powdery mildew are among the most serious diseases that affect grapevine. They can cause severe damage, such as yield loss, and affect the size of the grapes and their ability to accumulate sugars, affecting the flavor and aroma negatively and increasing the need for fungicidal sprays to combat these diseases and the pathogens that cause them. Clearly, it is important to predict these diseases early and apply treatment promptly to prevent and mitigate the effects of these diseases on crop production. This study presents a workflow in which IoT environmental sensors and machine learning methods are leveraged to accurately predict disease onset and allow for timely fungicide applications or other disease management strategies. We collected IoT grapevine field measurements and leveraged the records of the respective time periods during which fungicide treatments were applied to grapevine, and we used them to train and evaluate different ML tabular data classifiers as early predictors for each of the two diseases. The TabPFN transformer demonstrated superior performance in disease risk assessment while enabling real-time predictions with sub-second latencies, so it can be considered as a very good choice for a real-time grapevine disease prediction system.

The present dataset is a collection of multispectral images designed for development of detection algorithms for grapevine diseases like Flavescence dorée (FD) and Esca (ED). Although FD severely threatens viticulture, there are few public datasets and none with multispectral data collected in the field. The collected images have been taken from a frontal perspective of vineyard plants that highlights details of leaves and trunks facilitating detailed disease analysis. The data were collected using a Micasense RedEdge-P multispectral camera, capturing six spectral bands across 172 image captures of three different grapevine varieties used in Lambrusco wines: Ancellotta, Marani, and Salamino. The dataset includes raw and processed images, calibration images for the multispectral camera, annotations detailing plant health conditions, and Python-based usage examples for researchers. Potential applications include the development of machine learning algorithms for automated disease detection, image alignment techniques, and background removal methods. The dataset is a valuable resource for advancing remote and proximal sensing in precision agriculture.

Plant diseases caused by pathogens, that affect vineyards can cause serious damage and lead to reduced quantity and quality of fruits. Specifically for grapevines, diseases such as downy mildew and powdery mildew can cause yield loss, affect the size of the grapes, their ability to accumulate sugars affecting the flavor and aroma negatively and increase the need for fungicidal sprays to come up with these diseases and the pathogens that cause them. Clearly, it is important to early predict these diseases and timely apply treatment to mitigate the effects of diseases for the crop production. This study presents a workflow in which IoT environmental sensors and Machine Learning methods are leveraged for the early prediction of grapevine diseases, to accurately predict disease onset that allows for timely fungicide applications or other disease management strategies.