A randomized fractional modeling of pest management dynamics for tea plant (Camellia sinensis) with parameter adaptation to Turkish data

Fractional differential equations offer a detailed analysis of modeling problems. Many pest management models have been investigated by using fractional calculus and although these models provide a generalized approach, the inability to handle uncertainty highlights the need to incorporate probabilistic elements. In this study, a model of pest management is used to show that using randomized components along fractional derivatives provides an approach that handles the random nature of pest spread. The new model is thoroughly analyzed to validate the generalization achieved with the fractional-random approach. Results show that a 10% uncertainty in model components leads to 10 times more deviation in pest population. Similar findings on variation show that the fractional-random model is more competent in capturing the variability that is more significant for tea plant biomass. These results, which cannot be achieved with the deterministic model, indicate that the fractional-random model provides extensive findings that represent the variability in farmer awareness and pest spread tendencies.