Late detection of fungal infection is the main cause of inadequate disease control, affecting fruit quality and reducing yield of grapevine. Therefore, infrared imagery as a remote sensing technique was investigated in this study as a potential tool for early disease detection. Experiments were conducted under field conditions, and the effects of temporal and spatial variability in the leaf temperature of grapevine infected by Plasmopara viticola were studied. Evidence of the grapevine’s thermal response is a 3.2 °C increase in leaf temperature that occurred long before visible symptoms appeared. In our study, a correlation of R² = 0.76 at high significance level (p ≤ 0.001) was found between disease severity and MTD. Since the pathogen attack alters plant metabolic activities and stomatal conductance, the sensitivity of leaf temperature to leaf transpiration is high and can be used to monitor irregularities in temperature at an early stage of pathogen development.

Downy mildew is, globally, one of the most significant diseases in viticulture. Control of this pathogen is achieved through fungicide application. However, due to restrictions (from upcoming regulations) and growing environmental conscientiousness, it is critical to continuously enhance forecasting models to reduce fungicide application. Infection potential has traditionally been based on a 50 h–degree calculation (temperature multiplied by leaf wetness duration) measured by weather stations; the main climatic parameters for forecast modelling are temperature, relative humidity, and leaf wetness. This study took these parameters measured by a weather station and compared them with the same parameters measured inside a grape canopy. The study showed that the temperature readings by the weather station compared to inside the canopy recorded differences during the day but not at night; the relative humidity showed significant differences during both daytime and night; leaf wetness showed the highest differences and was statistically significant during both daytime and night. In conclusion, the measurement differences between inside of the canopy and at the weather station have significant impacts on the precision of forecasting models. Thus, using data from inside of a canopy for the prediction should lead to even less fungicide applications.

Grapevine downy mildew, caused by the oomycete Plasmopara viticola, affects most of the world’s finest and widely planted grapevine (Vitis vinifera L.) cultivars and is typically controlled by fungicides, which are often applied in excess. Reducing fungicide use through early pathogen detection is therefore a major goal, which could be supported by emerging optical remote sensing techniques that detect subtle changes in leaf phenotype associated with disease. However, considerable variability in grapevine leaf structures, both within and across varieties, poses a major challenge in the application of optical remote sensing techniques for early downy mildew detection. To address this problem, we present a simple and effective method for the artificial and precise infection of P. viticola on living grapevines, enabling scheduled infections with an average incubation period of five days. This method is non-destructive, highly targeted, and differs from existing techniques by avoiding leaf removal and diffuse infection patterns, while being fast and easy to perform. Restricting infection to small, defined areas on individual leaves enables direct comparisons between healthy and infected tissue within the same leaf, minimising variability effects. This precise infection protocol offers significant advantages for training remote sensing applications, developing pathogen detection models, and supporting detailed analyses of P. viticola development, contributing to improved early detection through normal visualisation methods and emerging optical remote sensing techniques and a potential reduction in fungicide use.