Ever-growing concerns and governmental restrictions related to the use of pesticides in modern agriculture has driven the need for more adept decision-making tools to minimize unnecessary treatments whilst still efficiently preventing a spread of infection. To this effect, a network of cost-effective, laser-based holographic detectors were developed and placed in vineyards in Switzerland and France with the objective of detecting and identifying airborne spores of downy and powdery mildew before they have the potential to infect crops. The data collected are remotely sent to a server where image processing techniques and artificial intelligence classify the spores and determine the quantitative intervention thresholds. Knowledge on the quantitative development of fungal diseases has been successfully used to temporally and spatially identify the primary infection of downy mildew which was confirmed by a visual evaluation of symptoms within the parcel. This data coupled with the current risk prediction models provide farmers with a powerful decision-making tool to optimise strategies in the management of grapevine diseases. 1 Introduction Environmentally friendly treatment plans and products, marker-assisted selection of resistant phenotypes and more precise decision-making technologies are some of the significant efforts developed to tackle governmental regulation strategies dedicated to the reduction of pesticides in agriculture. Their use impacts the soil, air, human health, and final product costs and quality [1]. In this study, we consider two major grapevine diseases; downy and powdery mildew caused by the oomycete Plasmopara viticola (PV) and the ascomycete Erysiphe necator (EN) respectively. These polycyclic pathogens are characterised by having a fast asexual cycle leading to the production, release, and dispersion of spores in the environment [2]. To treat the crops more selectively in time and space, efforts
