In the study of plant disease epidemics, there is often an interest in the state at the individual level to analyze disease progression, where the spatial structure of the population should be considered. Based on the disease status of the individuals, we developed a model to simulate plant disease spread in time and space, where the spatial dependence was introduced through the Matérn correlation function. Infection of a susceptible individual depended on the force of infection, given by the transmission rate of infected individuals and the spatial correlation. The algorithm for disease spread was implemented in Python, which allowed simulations of large populations to be handled efficiently. Using almond leaf scorch disease, caused by the bacterium Xylella fastidiosa, as a case study, the behavior of the model parameters and the type of initial introduction (random or aggregated) on disease spread was tested by simulation, as well as the effect of introducing and/or removing infected individuals. The greatest variability in results depended on the range parameter of the Matérn correlation function and type of initial introduction. In areas with high population density, the spread rate was almost constant, while regions without trees acted as a barrier to spread when the scope of their extent was greater than the value of the range parameter. This individual-based model can be also applied to other plant diseases, adapting the parameter values to their particular epidemiological characteristics.