Abstract

The biomathematical models have had limited influence in decision-making for managing and controlling fungal pathogens, such as grey mold. This lack of use is attributed to the difficulty in interpreting and bridging the gap between theory and experimentation due to the abstraction of these models. The digitization of these models offers new opportunities to harness experimental knowledge, especially in predicting the spread of grey mold in the vineyard. In this research, we have developed a predictive model incorporating data from phenological, meteorological, and epidemiological stages to describe the progression of infection in three clusters: susceptible (S), exposed (E), and infected (I). Through this model, we calculate the basic reproductive number (R0), enabling us to establish alert systems to halt the disease's spread. Subsequently, these models were digitized using Matlab GUI tools, allowing us to assess two types of scenarios (R0 > 1 and R0 < 1) to analyze the impact of key variables such as temperature, humidity, and the presence of Botrytis spores on disease incidence and severity. Furthermore, digitizing these models facilitates real-time data integration, including information from automated weather stations and field sensors. This enhances prediction accuracy and responsiveness to environmental changes. The developed model and its eventual validation emerge as a valuable tool for viticulturists and oenologists. It provides crucial information enabling them to make informed decisions regarding Botrytis management and control practices, thereby contributing to the enhancement of vineyard productivity.