Technical Abstract: Sequential sampling models for estimation and classification of the incidence of powdery mildew (caused by Podosphaera macularis) on hop (Humulus lupulus) cones were developed using parameter estimates of the binary power law derived from the analysis of 221 transect data sets (model construction data set). Stop lines for sequential estimation models were validated by bootstrap simulation using a subset of 21 model construction data sets, and simulated sampling of an additional 13 model construction data sets. Achieved coefficient of variation (C) approached the pre-specified C as disease incidence, p, increased, although achieving C = 0.1 was not possible for data sets where p< 0.03 with number of sampling units considered in this study. The 95% confidence interval of the median difference in p of the yard achieved by sequential sampling and the true p of the data set included zero for all 21 data sets evaluated at both levels of C = 0.1 and 0.2. For sequential classification, operating characteristic (OC) and average sample number (ASN) curves of the sequential sampling plans obtained by bootstrap analysis and simulated sampling were similar to OC and ASN values determined by Monte Carlo simulation. Correct decisions were made for 84.6 or 100% of data sets during simulated sampling when stop lines were determined assuming a binomial or beta-binomial distribution of disease incidence, respectively. However, the greater correct decision rate obtained by assuming a beta-binomial distribution of disease incidence required, on average, the sampling of 3.9 more plants to classify disease incidence, compared to the binomial distribution. Use of these sequential sampling plans may aid growers in deciding the harvest order of hop yards to minimize the risk of cone early maturity caused by late season by P. macularis, and aid in research efforts for when cones are assessed for hop powdery mildew.