Historically, interactions between cereal rust pathogens and their hosts have been studied using detailed visual assessments of the percent leaf area affected and ‘infection type’ classifications. In recent years new technologies in image analysis and molecular biology have been applied to the study of plant diseases, reducing rater bias and improving measurement resolution. We have developed a molecular assay to measure the growth of the crown rust pathogen within oat by estimating the fungal DNA content in total DNA extracted from infected oat tissue via quantitative PCR (q-PCR). Using this assay, differences between resistant, partially resistant, and susceptible genotypes were more distinct when compared to either visual estimations or digital image analysis, and the q-PCR method detected these differences before symptoms and signs appeared. The q-PCR method was used to assess crown rust resistance in a recombinant inbred mapping population. When data from 4 replicates was used separately to map the location of a major resistance (r) gene, the q-PCR method mapped the locus more precisely than either visual disease assessment or analysis of digital images (i.e. lower standard deviation of the distance between a fixed marker and the r gene). Furthermore, with composite interval mapping, the q-PCR method detected QTL for resistance more precisely than visual assessment (sharper peak and better correspondence to two-point linkage analysis) and detected QTL that were not discernable with other assessment methods. We believe the new method represents an improvement over conventional disease assessment techniques and that it will prove particularly useful in characterizing and mapping resistance. Work is underway to compare assessments using the new assay with the results of costly multiple location field tests with respect to their relative ability to identify partial resistance QTL for oat crown rust.