The Japanese beetle, Popillia japonica Newman (Coleoptera: Scarabaeidae), an invasive species from northern Japan, was first detected in Minnesota in 1968. According to fruit growers and the Minnesota Department of Agriculture, population size and feeding damage has been an increasing concern since 2010. Based on trap-catch data, populations have recently exceeded 4,000 beetles/trap/week during July-August near raspberry fields, and can increase by an order of magnitude within 7-10 days. The primary goals of this study were to assess the spatial distribution of P. japonica adults in raspberry, and to develop and validate a practical fixed-precision sequential sampling plan for grower use. Taylor's Power Law (TPL) regression was used to characterize the beetle's spatial pattern in research plots and commercial fields, either with or without insecticide applications. We then used Green's plan to develop an enumerative sequential sampling plan to estimate P. japonica density in primocane raspberry. Beetle population data were collected at two locations in southern Minnesota, including the Rosemount Research and Outreach Center, and a commercial field near Forest Lake. The TPL results, via slope comparisons, indicated no significant differences in P. japonica spatial pattern between insecticide treated plots versus untreated plots, or among 4 different insecticides (P>0.05). Utilizing all spatial pattern data, we characterized the distribution of P. japonica beetles to be highly aggregated in raspberry, with TPL slopes ranging from b = 1.38 to 1.55; all slopes were found to be >1.0. Although the slopes were not significantly different, we accounted for variability in spatial pattern by using 33 independent data sets, and the Resampling for Validation of Sampling Plans (RVSP) model to validate a sampling plan with a final average precision level of 0.25 (SEM/mean), recommended for integrated pest management (IPM) purposes. The final sampling plan required an average sample number of only 15, 1-m-row samples, while providing high relative net precision (RNP), and thus a cost-effective, efficient sample plan for growers.