Improving a weed population model using the sequential Monte Carlo method

Field measurements and weed population model predictions have been proposed as the basis for recommendations requiring chemical or mechanical treatment, but both approaches have some limitations. This study shows how a sequential Monte Carlo (SMC) method can be used to combine weed measurements and model predictions to better estimate weed characteristics. SMC was applied to the dynamic model, which simulates weed density, seed production, and seedbank density for Alopecurus myosuaroids (blackgrass). Using experiments conducted in seven plots over the course of 6 years, the benefit from SMC was determined for several types of weed count data. Compared to the initial model predictions, SMC reduced the root mean squared error (RMSE) by 33.5–81.5%. Compared to the weed densities obtained from weed counts alone, SMC reduced the RMSE by 1.2–10%. SMC should be preferred for single use of the model or weed count data, as it can improve weed density predictions and because to analyze uncertainty about the status of the probability distribution system calculated by SMC Can be used.