Leaf Wetness Duration (LWD) is a key parameter in agricultural meteorology. Because of difficulties involved in LWD being readily measured, several methods have been developed to estimate it from weather data. Among the employed to estimate LWD, those that use physical principles of dew formation plus dew and/or rain evaporation have shown to be especially transmittable and of sufficiently accurate results, but their complexity is a disadvantage for operational use. Alternatively, empirical models have been utilized despite their limitations. The simplest empirical models use only relative humidity data. The objective of this study was to evaluate the performance of an RH-based empirical model and a Penman-Monteith physical model to estimate LWD in Sarvestan automatic station located in Fars province. The results indicated that both models during warm seasons underestimate LWD, while during cold seasons, the physical and empirical models show overestimation and underestimation, respectively. The Mean Absolute Error (MAE) in empirical model was recorded to be less than that in physical model's estimations. An adjusted optimum threshold value of relative humidity was suggested for the study which improved the estimations. Using an RH-based empirical model led to more accurate LWD estimations with less errors as compared with the previous published data. Hourly comparisons also showed that the optimum threshold model was of a more acceptable performance as compared with the other models.